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Weiye Loh

A Scientist Takes On Gravity - NYTimes.com - 0 views

www.nytimes.com/...13gravity.html

Gravity Science

shared by Weiye Loh on 28 Jul 10 - Cached
  • Erik Verlinde, 48, a respected string theorist and professor of physics at the University of Amsterdam, whose contention that gravity is indeed an illusion has caused a continuing ruckus among physicists, or at least among those who profess to understand it. Reversing the logic of 300 years of science, he argued in a recent paper, titled “On the Origin of Gravity and the Laws of Newton,” that gravity is a consequence of the venerable laws of thermodynamics, which describe the behavior of heat and gases.
  • “For me gravity doesn’t exist,” said Dr. Verlinde, who was recently in the United States to explain himself. Not that he can’t fall down, but Dr. Verlinde is among a number of physicists who say that science has been looking at gravity the wrong way and that there is something more basic, from which gravity “emerges,” the way stock markets emerge from the collective behavior of individual investors or that elasticity emerges from the mechanics of atoms.
  • Looking at gravity from this angle, they say, could shed light on some of the vexing cosmic issues of the day, like the dark energy, a kind of anti-gravity that seems to be speeding up the expansion of the universe, or the dark matter that is supposedly needed to hold galaxies together.
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  • Some of the best physicists in the world say they don’t understand Dr. Verlinde’s paper, and many are outright skeptical. But some of those very same physicists say he has provided a fresh perspective on some of the deepest questions in science, namely why space, time and gravity exist at all — even if he has not yet answered them.
  • “Some people have said it can’t be right, others that it’s right and we already knew it — that it’s right and profound, right and trivial,” Andrew Strominger, a string theorist at Harvard said. “What you have to say,” he went on, “is that it has inspired a lot of interesting discussions. It’s just a very interesting collection of ideas that touch on things we most profoundly do not understand about our universe. That’s why I liked it.”
  • You might wonder why a string theorist is interested in Newton’s equations. After all Newton was overturned a century ago by Einstein, who explained gravity as warps in the geometry of space-time, and who some theorists think could be overturned in turn by string theorists. Over the last 30 years gravity has been “undressed,” in Dr. Verlinde’s words, as a fundamental force. This disrobing began in the 1970s with the discovery by Jacob Bekenstein of the Hebrew University of Jerusalem and Stephen Hawking of Cambridge University, among others, of a mysterious connection between black holes and thermodynamics, culminating in Dr. Hawking’s discovery in 1974 that when quantum effects are taken into account black holes would glow and eventually explode.
  • Weiye Loh on 28 Jul 10
     
    A Scientist Takes On Gravity
Weiye Loh

The Decline Effect and the Scientific Method : The New Yorker - 0 views

www.newyorker.com/...101213fa_fact_lehrer

Truth Science Evidence Data

shared by Weiye Loh on 05 Jan 11 - No Cached
  • On September 18, 2007, a few dozen neuroscientists, psychiatrists, and drug-company executives gathered in a hotel conference room in Brussels to hear some startling news. It had to do with a class of drugs known as atypical or second-generation antipsychotics, which came on the market in the early nineties.
  • the therapeutic power of the drugs appeared to be steadily waning. A recent study showed an effect that was less than half of that documented in the first trials, in the early nineteen-nineties. Many researchers began to argue that the expensive pharmaceuticals weren’t any better than first-generation antipsychotics, which have been in use since the fifties. “In fact, sometimes they now look even worse,” John Davis, a professor of psychiatry at the University of Illinois at Chicago, told me.
  • Before the effectiveness of a drug can be confirmed, it must be tested and tested again. Different scientists in different labs need to repeat the protocols and publish their results. The test of replicability, as it’s known, is the foundation of modern research. Replicability is how the community enforces itself. It’s a safeguard for the creep of subjectivity. Most of the time, scientists know what results they want, and that can influence the results they get. The premise of replicability is that the scientific community can correct for these flaws.
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  • But now all sorts of well-established, multiply confirmed findings have started to look increasingly uncertain. It’s as if our facts were losing their truth: claims that have been enshrined in textbooks are suddenly unprovable. This phenomenon doesn’t yet have an official name, but it’s occurring across a wide range of fields, from psychology to ecology. In the field of medicine, the phenomenon seems extremely widespread, affecting not only antipsychotics but also therapies ranging from cardiac stents to Vitamin E and antidepressants: Davis has a forthcoming analysis demonstrating that the efficacy of antidepressants has gone down as much as threefold in recent decades.
  • the effect is especially troubling because of what it exposes about the scientific process. If replication is what separates the rigor of science from the squishiness of pseudoscience, where do we put all these rigorously validated findings that can no longer be proved? Which results should we believe? Francis Bacon, the early-modern philosopher and pioneer of the scientific method, once declared that experiments were essential, because they allowed us to “put nature to the question.” But it appears that nature often gives us different answers.
  • At first, he assumed that he’d made an error in experimental design or a statistical miscalculation. But he couldn’t find anything wrong with his research. He then concluded that his initial batch of research subjects must have been unusually susceptible to verbal overshadowing. (John Davis, similarly, has speculated that part of the drop-off in the effectiveness of antipsychotics can be attributed to using subjects who suffer from milder forms of psychosis which are less likely to show dramatic improvement.) “It wasn’t a very satisfying explanation,” Schooler says. “One of my mentors told me that my real mistake was trying to replicate my work. He told me doing that was just setting myself up for disappointment.”
  • In private, Schooler began referring to the problem as “cosmic habituation,” by analogy to the decrease in response that occurs when individuals habituate to particular stimuli. “Habituation is why you don’t notice the stuff that’s always there,” Schooler says. “It’s an inevitable process of adjustment, a ratcheting down of excitement. I started joking that it was like the cosmos was habituating to my ideas. I took it very personally.”
  • The most likely explanation for the decline is an obvious one: regression to the mean. As the experiment is repeated, that is, an early statistical fluke gets cancelled out. The extrasensory powers of Schooler’s subjects didn’t decline—they were simply an illusion that vanished over time. And yet Schooler has noticed that many of the data sets that end up declining seem statistically solid—that is, they contain enough data that any regression to the mean shouldn’t be dramatic. “These are the results that pass all the tests,” he says. “The odds of them being random are typically quite remote, like one in a million. This means that the decline effect should almost never happen. But it happens all the time!
  • this is why Schooler believes that the decline effect deserves more attention: its ubiquity seems to violate the laws of statistics. “Whenever I start talking about this, scientists get very nervous,” he says. “But I still want to know what happened to my results. Like most scientists, I assumed that it would get easier to document my effect over time. I’d get better at doing the experiments, at zeroing in on the conditions that produce verbal overshadowing. So why did the opposite happen? I’m convinced that we can use the tools of science to figure this out. First, though, we have to admit that we’ve got a problem.”
  • In 2001, Michael Jennions, a biologist at the Australian National University, set out to analyze “temporal trends” across a wide range of subjects in ecology and evolutionary biology. He looked at hundreds of papers and forty-four meta-analyses (that is, statistical syntheses of related studies), and discovered a consistent decline effect over time, as many of the theories seemed to fade into irrelevance. In fact, even when numerous variables were controlled for—Jennions knew, for instance, that the same author might publish several critical papers, which could distort his analysis—there was still a significant decrease in the validity of the hypothesis, often within a year of publication. Jennions admits that his findings are troubling, but expresses a reluctance to talk about them publicly. “This is a very sensitive issue for scientists,” he says. “You know, we’re supposed to be dealing with hard facts, the stuff that’s supposed to stand the test of time. But when you see these trends you become a little more skeptical of things.”
  • the worst part was that when I submitted these null results I had difficulty getting them published. The journals only wanted confirming data. It was too exciting an idea to disprove, at least back then.
  • the steep rise and slow fall of fluctuating asymmetry is a clear example of a scientific paradigm, one of those intellectual fads that both guide and constrain research: after a new paradigm is proposed, the peer-review process is tilted toward positive results. But then, after a few years, the academic incentives shift—the paradigm has become entrenched—so that the most notable results are now those that disprove the theory.
  • Jennions, similarly, argues that the decline effect is largely a product of publication bias, or the tendency of scientists and scientific journals to prefer positive data over null results, which is what happens when no effect is found. The bias was first identified by the statistician Theodore Sterling, in 1959, after he noticed that ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for. A “significant” result is defined as any data point that would be produced by chance less than five per cent of the time. This ubiquitous test was invented in 1922 by the English mathematician Ronald Fisher, who picked five per cent as the boundary line, somewhat arbitrarily, because it made pencil and slide-rule calculations easier. Sterling saw that if ninety-seven per cent of psychology studies were proving their hypotheses, either psychologists were extraordinarily lucky or they published only the outcomes of successful experiments. In recent years, publication bias has mostly been seen as a problem for clinical trials, since pharmaceutical companies are less interested in publishing results that aren’t favorable. But it’s becoming increasingly clear that publication bias also produces major distortions in fields without large corporate incentives, such as psychology and ecology.
  • While publication bias almost certainly plays a role in the decline effect, it remains an incomplete explanation. For one thing, it fails to account for the initial prevalence of positive results among studies that never even get submitted to journals. It also fails to explain the experience of people like Schooler, who have been unable to replicate their initial data despite their best efforts
  • an equally significant issue is the selective reporting of results—the data that scientists choose to document in the first place. Palmer’s most convincing evidence relies on a statistical tool known as a funnel graph. When a large number of studies have been done on a single subject, the data should follow a pattern: studies with a large sample size should all cluster around a common value—the true result—whereas those with a smaller sample size should exhibit a random scattering, since they’re subject to greater sampling error. This pattern gives the graph its name, since the distribution resembles a funnel.
  • The funnel graph visually captures the distortions of selective reporting. For instance, after Palmer plotted every study of fluctuating asymmetry, he noticed that the distribution of results with smaller sample sizes wasn’t random at all but instead skewed heavily toward positive results.
  • Palmer has since documented a similar problem in several other contested subject areas. “Once I realized that selective reporting is everywhere in science, I got quite depressed,” Palmer told me. “As a researcher, you’re always aware that there might be some nonrandom patterns, but I had no idea how widespread it is.” In a recent review article, Palmer summarized the impact of selective reporting on his field: “We cannot escape the troubling conclusion that some—perhaps many—cherished generalities are at best exaggerated in their biological significance and at worst a collective illusion nurtured by strong a-priori beliefs often repeated.”
  • Palmer emphasizes that selective reporting is not the same as scientific fraud. Rather, the problem seems to be one of subtle omissions and unconscious misperceptions, as researchers struggle to make sense of their results. Stephen Jay Gould referred to this as the “shoehorning” process. “A lot of scientific measurement is really hard,” Simmons told me. “If you’re talking about fluctuating asymmetry, then it’s a matter of minuscule differences between the right and left sides of an animal. It’s millimetres of a tail feather. And so maybe a researcher knows that he’s measuring a good male”—an animal that has successfully mated—“and he knows that it’s supposed to be symmetrical. Well, that act of measurement is going to be vulnerable to all sorts of perception biases. That’s not a cynical statement. That’s just the way human beings work.”
  • One of the classic examples of selective reporting concerns the testing of acupuncture in different countries. While acupuncture is widely accepted as a medical treatment in various Asian countries, its use is much more contested in the West. These cultural differences have profoundly influenced the results of clinical trials. Between 1966 and 1995, there were forty-seven studies of acupuncture in China, Taiwan, and Japan, and every single trial concluded that acupuncture was an effective treatment. During the same period, there were ninety-four clinical trials of acupuncture in the United States, Sweden, and the U.K., and only fifty-six per cent of these studies found any therapeutic benefits. As Palmer notes, this wide discrepancy suggests that scientists find ways to confirm their preferred hypothesis, disregarding what they don’t want to see. Our beliefs are a form of blindness.
  • John Ioannidis, an epidemiologist at Stanford University, argues that such distortions are a serious issue in biomedical research. “These exaggerations are why the decline has become so common,” he says. “It’d be really great if the initial studies gave us an accurate summary of things. But they don’t. And so what happens is we waste a lot of money treating millions of patients and doing lots of follow-up studies on other themes based on results that are misleading.”
  • In 2005, Ioannidis published an article in the Journal of the American Medical Association that looked at the forty-nine most cited clinical-research studies in three major medical journals. Forty-five of these studies reported positive results, suggesting that the intervention being tested was effective. Because most of these studies were randomized controlled trials—the “gold standard” of medical evidence—they tended to have a significant impact on clinical practice, and led to the spread of treatments such as hormone replacement therapy for menopausal women and daily low-dose aspirin to prevent heart attacks and strokes. Nevertheless, the data Ioannidis found were disturbing: of the thirty-four claims that had been subject to replication, forty-one per cent had either been directly contradicted or had their effect sizes significantly downgraded.
  • The situation is even worse when a subject is fashionable. In recent years, for instance, there have been hundreds of studies on the various genes that control the differences in disease risk between men and women. These findings have included everything from the mutations responsible for the increased risk of schizophrenia to the genes underlying hypertension. Ioannidis and his colleagues looked at four hundred and thirty-two of these claims. They quickly discovered that the vast majority had serious flaws. But the most troubling fact emerged when he looked at the test of replication: out of four hundred and thirty-two claims, only a single one was consistently replicable. “This doesn’t mean that none of these claims will turn out to be true,” he says. “But, given that most of them were done badly, I wouldn’t hold my breath.”
  • the main problem is that too many researchers engage in what he calls “significance chasing,” or finding ways to interpret the data so that it passes the statistical test of significance—the ninety-five-per-cent boundary invented by Ronald Fisher. “The scientists are so eager to pass this magical test that they start playing around with the numbers, trying to find anything that seems worthy,” Ioannidis says. In recent years, Ioannidis has become increasingly blunt about the pervasiveness of the problem. One of his most cited papers has a deliberately provocative title: “Why Most Published Research Findings Are False.”
  • The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong. “It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it. And that’s why, even after a claim has been systematically disproven”—he cites, for instance, the early work on hormone replacement therapy, or claims involving various vitamins—“you still see some stubborn researchers citing the first few studies that show a strong effect. They really want to believe that it’s true.”
  • scientists need to become more rigorous about data collection before they publish. “We’re wasting too much time chasing after bad studies and underpowered experiments,” he says. The current “obsession” with replicability distracts from the real problem, which is faulty design. He notes that nobody even tries to replicate most science papers—there are simply too many. (According to Nature, a third of all studies never even get cited, let alone repeated.)
  • Schooler recommends the establishment of an open-source database, in which researchers are required to outline their planned investigations and document all their results. “I think this would provide a huge increase in access to scientific work and give us a much better way to judge the quality of an experiment,” Schooler says. “It would help us finally deal with all these issues that the decline effect is exposing.”
  • Although such reforms would mitigate the dangers of publication bias and selective reporting, they still wouldn’t erase the decline effect. This is largely because scientific research will always be shadowed by a force that can’t be curbed, only contained: sheer randomness. Although little research has been done on the experimental dangers of chance and happenstance, the research that exists isn’t encouraging
  • John Crabbe, a neuroscientist at the Oregon Health and Science University, conducted an experiment that showed how unknowable chance events can skew tests of replicability. He performed a series of experiments on mouse behavior in three different science labs: in Albany, New York; Edmonton, Alberta; and Portland, Oregon. Before he conducted the experiments, he tried to standardize every variable he could think of. The same strains of mice were used in each lab, shipped on the same day from the same supplier. The animals were raised in the same kind of enclosure, with the same brand of sawdust bedding. They had been exposed to the same amount of incandescent light, were living with the same number of littermates, and were fed the exact same type of chow pellets. When the mice were handled, it was with the same kind of surgical glove, and when they were tested it was on the same equipment, at the same time in the morning.
  • The premise of this test of replicability, of course, is that each of the labs should have generated the same pattern of results. “If any set of experiments should have passed the test, it should have been ours,” Crabbe says. “But that’s not the way it turned out.” In one experiment, Crabbe injected a particular strain of mouse with cocaine. In Portland the mice given the drug moved, on average, six hundred centimetres more than they normally did; in Albany they moved seven hundred and one additional centimetres. But in the Edmonton lab they moved more than five thousand additional centimetres. Similar deviations were observed in a test of anxiety. Furthermore, these inconsistencies didn’t follow any detectable pattern. In Portland one strain of mouse proved most anxious, while in Albany another strain won that distinction.
  • The disturbing implication of the Crabbe study is that a lot of extraordinary scientific data are nothing but noise. The hyperactivity of those coked-up Edmonton mice wasn’t an interesting new fact—it was a meaningless outlier, a by-product of invisible variables we don’t understand. The problem, of course, is that such dramatic findings are also the most likely to get published in prestigious journals, since the data are both statistically significant and entirely unexpected. Grants get written, follow-up studies are conducted. The end result is a scientific accident that can take years to unravel.
  • This suggests that the decline effect is actually a decline of illusion.
  • While Karl Popper imagined falsification occurring with a single, definitive experiment—Galileo refuted Aristotelian mechanics in an afternoon—the process turns out to be much messier than that. Many scientific theories continue to be considered true even after failing numerous experimental tests. Verbal overshadowing might exhibit the decline effect, but it remains extensively relied upon within the field. The same holds for any number of phenomena, from the disappearing benefits of second-generation antipsychotics to the weak coupling ratio exhibited by decaying neutrons, which appears to have fallen by more than ten standard deviations between 1969 and 2001. Even the law of gravity hasn’t always been perfect at predicting real-world phenomena. (In one test, physicists measuring gravity by means of deep boreholes in the Nevada desert found a two-and-a-half-per-cent discrepancy between the theoretical predictions and the actual data.) Despite these findings, second-generation antipsychotics are still widely prescribed, and our model of the neutron hasn’t changed. The law of gravity remains the same.
  • Such anomalies demonstrate the slipperiness of empiricism. Although many scientific ideas generate conflicting results and suffer from falling effect sizes, they continue to get cited in the textbooks and drive standard medical practice. Why? Because these ideas seem true. Because they make sense. Because we can’t bear to let them go. And this is why the decline effect is so troubling. Not because it reveals the human fallibility of science, in which data are tweaked and beliefs shape perceptions. (Such shortcomings aren’t surprising, at least for scientists.) And not because it reveals that many of our most exciting theories are fleeting fads and will soon be rejected. (That idea has been around since Thomas Kuhn.) The decline effect is troubling because it reminds us how difficult it is to prove anything. We like to pretend that our experiments define the truth for us. But that’s often not the case. Just because an idea is true doesn’t mean it can be proved. And just because an idea can be proved doesn’t mean it’s true. When the experiments are done, we still have to choose what to believe.
Weiye Loh

The Mysterious Decline Effect | Wired Science | Wired.com - 0 views

www.wired.com/...the-mysterious-decline-effect

Truth Science Evidence Data

shared by Weiye Loh on 05 Jan 11 - No Cached
  • Question #1: Does this mean I don’t have to believe in climate change? Me: I’m afraid not. One of the sad ironies of scientific denialism is that we tend to be skeptical of precisely the wrong kind of scientific claims. In poll after poll, Americans have dismissed two of the most robust and widely tested theories of modern science: evolution by natural selection and climate change. These are theories that have been verified in thousands of different ways by thousands of different scientists working in many different fields. (This doesn’t mean, of course, that such theories won’t change or get modified – the strength of science is that nothing is settled.) Instead of wasting public debate on creationism or the rhetoric of Senator Inhofe, I wish we’d spend more time considering the value of spinal fusion surgery, or second generation antipsychotics, or the verity of the latest gene association study. The larger point is that we need to be a better job of considering the context behind every claim. In 1952, the Harvard philosopher Willard Von Orman published “The Two Dogmas of Empiricism.” In the essay, Quine compared the truths of science to a spider’s web, in which the strength of the lattice depends upon its interconnectedness. (Quine: “The unit of empirical significance is the whole of science.”) One of the implications of Quine’s paper is that, when evaluating the power of a given study, we need to also consider the other studies and untested assumptions that it depends upon. Don’t just fixate on the effect size – look at the web. Unfortunately for the denialists, climate change and natural selection have very sturdy webs.
  • biases are not fraud. We sometimes forget that science is a human pursuit, mingled with all of our flaws and failings. (Perhaps that explains why an episode like Climategate gets so much attention.) If there’s a single theme that runs through the article it’s that finding the truth is really hard. It’s hard because reality is complicated, shaped by a surreal excess of variables. But it’s also hard because scientists aren’t robots: the act of observation is simultaneously an act of interpretation.
  • (As Paul Simon sang, “A man sees what he wants to see and disregards the rest.”) Most of the time, these distortions are unconscious – we don’t know even we are misperceiving the data. However, even when the distortion is intentional it’s still rarely rises to the level of outright fraud. Consider the story of Mike Rossner. He’s executive director of the Rockefeller University Press, and helps oversee several scientific publications, including The Journal of Cell Biology.  In 2002, while trying to format a scientific image in Photoshop that was going to appear in one of the journals, Rossner noticed that the background of the image contained distinct intensities of pixels. “That’s a hallmark of image manipulation,” Rossner told me. “It means the scientist has gone in and deliberately changed what the data looks like. What’s disturbing is just how easy this is to do.” This led Rossner and his colleagues to begin analyzing every image in every accepted paper. They soon discovered that approximately 25 percent of all papers contained at least one “inappropriately manipulated” picture. Interestingly, the vast, vast majority of these manipulations (~99 percent) didn’t affect the interpretation of the results. Instead, the scientists seemed to be photoshopping the pictures for aesthetic reasons: perhaps a line on a gel was erased, or a background blur was deleted, or the contrast was exaggerated. In other words, they wanted to publish pretty images. That’s a perfectly understandable desire, but it gets problematic when that same basic instinct – we want our data to be neat, our pictures to be clean, our charts to be clear – is transposed across the entire scientific process.
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  • One of the philosophy papers that I kept on thinking about while writing the article was Nancy Cartwright’s essay “Do the Laws of Physics State the Facts?” Cartwright used numerous examples from modern physics to argue that there is often a basic trade-off between scientific “truth” and experimental validity, so that the laws that are the most true are also the most useless. “Despite their great explanatory power, these laws [such as gravity] do not describe reality,” Cartwright writes. “Instead, fundamental laws describe highly idealized objects in models.”  The problem, of course, is that experiments don’t test models. They test reality.
  • Cartwright’s larger point is that many essential scientific theories – those laws that explain things – are not actually provable, at least in the conventional sense. This doesn’t mean that gravity isn’t true or real. There is, perhaps, no truer idea in all of science. (Feynman famously referred to gravity as the “greatest generalization achieved by the human mind.”) Instead, what the anomalies of physics demonstrate is that there is no single test that can define the truth. Although we often pretend that experiments and peer-review and clinical trials settle the truth for us – that we are mere passive observers, dutifully recording the results – the actuality of science is a lot messier than that. Richard Rorty said it best: “To say that we should drop the idea of truth as out there waiting to be discovered is not to say that we have discovered that, out there, there is no truth.” Of course, the very fact that the facts aren’t obvious, that the truth isn’t “waiting to be discovered,” means that science is intensely human. It requires us to look, to search, to plead with nature for an answer.
Weiye Loh

YouTube - I'm A Climate Scientist (HUNGRY BEAST) - 0 views

www.youtube.com/watch

Climate Science

shared by Weiye Loh on 19 May 11 - No Cached
  • Weiye Loh on 19 May 11
     
    yo....we're climate scientists.. and there's no denying this Climate Change Is REEEEALL.. Who's a climate scientist.. I'm a climate scientist.. Not a cleo finalist No a climate scientist Droppin facts all over this wax While bitches be crying about a carbon tax Climate change is caused by people Earth Unlike Alien Has no sequel We gotta move fast or we'll be forsaken, Cause we were too busy suckin dick Copenhagen: (Politician) I said Burn! it's hot in here.. 32% more carbon in the atmosphere. Oh Eee Ohh Eee oh wee ice ice ice Raisin' sea levels twice by twice We're scientists, what we speak is True. Unlike Andrew Bolt our work is Peer Reviewed... ooohhh Who's a climate scientist.. I'm a climate scientist.. An Anglican revivalist No a climate scientist Feedback is like climate change on crack The permafrosts subtracts: feedback Methane release wack : feedback.. Write a letter then burn it: feedback Denialists deny this in your dreams Coz climate change means greater extremes, Shit won't be the norm Heatwaves bigger badder storms The Green house effect is just a theory sucker (Alan Jones) Yeah so is gravity float away muther f**cker Who's a climate scientist.. I'm a climate scientist.. I'm not a climate Scientist Who's Climate Scientists A Penny Farthing Cyclist No A Lebanese typist No A Paleontologist No A Sebaceous Cyst No! a climate scientist! Yo! PREACH~!
Weiye Loh

Can a group of scientists in California end the war on climate change? | Science | The ... - 0 views

www.guardian.co.uk/...entists-end-climate-change-war

Climate Science Academic Research Data Mining Data Open Source Funding ClimateGate Polarisation Statistics

shared by Weiye Loh on 01 Mar 11 - No Cached
  • Muller calls his latest obsession the Berkeley Earth project. The aim is so simple that the complexity and magnitude of the undertaking is easy to miss. Starting from scratch, with new computer tools and more data than has ever been used, they will arrive at an independent assessment of global warming. The team will also make every piece of data it uses – 1.6bn data points – freely available on a website. It will post its workings alongside, including full information on how more than 100 years of data from thousands of instruments around the world are stitched together to give a historic record of the planet's temperature.
  • Muller is fed up with the politicised row that all too often engulfs climate science. By laying all its data and workings out in the open, where they can be checked and challenged by anyone, the Berkeley team hopes to achieve something remarkable: a broader consensus on global warming. In no other field would Muller's dream seem so ambitious, or perhaps, so naive.
  • "We are bringing the spirit of science back to a subject that has become too argumentative and too contentious," Muller says, over a cup of tea. "We are an independent, non-political, non-partisan group. We will gather the data, do the analysis, present the results and make all of it available. There will be no spin, whatever we find." Why does Muller feel compelled to shake up the world of climate change? "We are doing this because it is the most important project in the world today. Nothing else comes close," he says.
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  • There are already three heavyweight groups that could be considered the official keepers of the world's climate data. Each publishes its own figures that feed into the UN's Intergovernmental Panel on Climate Change. Nasa's Goddard Institute for Space Studies in New York City produces a rolling estimate of the world's warming. A separate assessment comes from another US agency, the National Oceanic and Atmospheric Administration (Noaa). The third group is based in the UK and led by the Met Office. They all take readings from instruments around the world to come up with a rolling record of the Earth's mean surface temperature. The numbers differ because each group uses its own dataset and does its own analysis, but they show a similar trend. Since pre-industrial times, all point to a warming of around 0.75C.
  • You might think three groups was enough, but Muller rolls out a list of shortcomings, some real, some perceived, that he suspects might undermine public confidence in global warming records. For a start, he says, warming trends are not based on all the available temperature records. The data that is used is filtered and might not be as representative as it could be. He also cites a poor history of transparency in climate science, though others argue many climate records and the tools to analyse them have been public for years.
  • Then there is the fiasco of 2009 that saw roughly 1,000 emails from a server at the University of East Anglia's Climatic Research Unit (CRU) find their way on to the internet. The fuss over the messages, inevitably dubbed Climategate, gave Muller's nascent project added impetus. Climate sceptics had already attacked James Hansen, head of the Nasa group, for making political statements on climate change while maintaining his role as an objective scientist. The Climategate emails fuelled their protests. "With CRU's credibility undergoing a severe test, it was all the more important to have a new team jump in, do the analysis fresh and address all of the legitimate issues raised by sceptics," says Muller.
  • This latest point is where Muller faces his most delicate challenge. To concede that climate sceptics raise fair criticisms means acknowledging that scientists and government agencies have got things wrong, or at least could do better. But the debate around global warming is so highly charged that open discussion, which science requires, can be difficult to hold in public. At worst, criticising poor climate science can be taken as an attack on science itself, a knee-jerk reaction that has unhealthy consequences. "Scientists will jump to the defence of alarmists because they don't recognise that the alarmists are exaggerating," Muller says.
  • The Berkeley Earth project came together more than a year ago, when Muller rang David Brillinger, a statistics professor at Berkeley and the man Nasa called when it wanted someone to check its risk estimates of space debris smashing into the International Space Station. He wanted Brillinger to oversee every stage of the project. Brillinger accepted straight away. Since the first meeting he has advised the scientists on how best to analyse their data and what pitfalls to avoid. "You can think of statisticians as the keepers of the scientific method, " Brillinger told me. "Can scientists and doctors reasonably draw the conclusions they are setting down? That's what we're here for."
  • For the rest of the team, Muller says he picked scientists known for original thinking. One is Saul Perlmutter, the Berkeley physicist who found evidence that the universe is expanding at an ever faster rate, courtesy of mysterious "dark energy" that pushes against gravity. Another is Art Rosenfeld, the last student of the legendary Manhattan Project physicist Enrico Fermi, and something of a legend himself in energy research. Then there is Robert Jacobsen, a Berkeley physicist who is an expert on giant datasets; and Judith Curry, a climatologist at Georgia Institute of Technology, who has raised concerns over tribalism and hubris in climate science.
  • Robert Rohde, a young physicist who left Berkeley with a PhD last year, does most of the hard work. He has written software that trawls public databases, themselves the product of years of painstaking work, for global temperature records. These are compiled, de-duplicated and merged into one huge historical temperature record. The data, by all accounts, are a mess. There are 16 separate datasets in 14 different formats and they overlap, but not completely. Muller likens Rohde's achievement to Hercules's enormous task of cleaning the Augean stables.
  • The wealth of data Rohde has collected so far – and some dates back to the 1700s – makes for what Muller believes is the most complete historical record of land temperatures ever compiled. It will, of itself, Muller claims, be a priceless resource for anyone who wishes to study climate change. So far, Rohde has gathered records from 39,340 individual stations worldwide.
  • Publishing an extensive set of temperature records is the first goal of Muller's project. The second is to turn this vast haul of data into an assessment on global warming.
  • The big three groups – Nasa, Noaa and the Met Office – work out global warming trends by placing an imaginary grid over the planet and averaging temperatures records in each square. So for a given month, all the records in England and Wales might be averaged out to give one number. Muller's team will take temperature records from individual stations and weight them according to how reliable they are.
  • This is where the Berkeley group faces its toughest task by far and it will be judged on how well it deals with it. There are errors running through global warming data that arise from the simple fact that the global network of temperature stations was never designed or maintained to monitor climate change. The network grew in a piecemeal fashion, starting with temperature stations installed here and there, usually to record local weather.
  • Among the trickiest errors to deal with are so-called systematic biases, which skew temperature measurements in fiendishly complex ways. Stations get moved around, replaced with newer models, or swapped for instruments that record in celsius instead of fahrenheit. The times measurements are taken varies, from say 6am to 9pm. The accuracy of individual stations drift over time and even changes in the surroundings, such as growing trees, can shield a station more from wind and sun one year to the next. Each of these interferes with a station's temperature measurements, perhaps making it read too cold, or too hot. And these errors combine and build up.
  • This is the real mess that will take a Herculean effort to clean up. The Berkeley Earth team is using algorithms that automatically correct for some of the errors, a strategy Muller favours because it doesn't rely on human interference. When the team publishes its results, this is where the scrutiny will be most intense.
  • Despite the scale of the task, and the fact that world-class scientific organisations have been wrestling with it for decades, Muller is convinced his approach will lead to a better assessment of how much the world is warming. "I've told the team I don't know if global warming is more or less than we hear, but I do believe we can get a more precise number, and we can do it in a way that will cool the arguments over climate change, if nothing else," says Muller. "Science has its weaknesses and it doesn't have a stranglehold on the truth, but it has a way of approaching technical issues that is a closer approximation of truth than any other method we have."
  • It might not be a good sign that one prominent climate sceptic contacted by the Guardian, Canadian economist Ross McKitrick, had never heard of the project. Another, Stephen McIntyre, whom Muller has defended on some issues, hasn't followed the project either, but said "anything that [Muller] does will be well done". Phil Jones at the University of East Anglia was unclear on the details of the Berkeley project and didn't comment.
  • Elsewhere, Muller has qualified support from some of the biggest names in the business. At Nasa, Hansen welcomed the project, but warned against over-emphasising what he expects to be the minor differences between Berkeley's global warming assessment and those from the other groups. "We have enough trouble communicating with the public already," Hansen says. At the Met Office, Peter Stott, head of climate monitoring and attribution, was in favour of the project if it was open and peer-reviewed.
  • Peter Thorne, who left the Met Office's Hadley Centre last year to join the Co-operative Institute for Climate and Satellites in North Carolina, is enthusiastic about the Berkeley project but raises an eyebrow at some of Muller's claims. The Berkeley group will not be the first to put its data and tools online, he says. Teams at Nasa and Noaa have been doing this for many years. And while Muller may have more data, they add little real value, Thorne says. Most are records from stations installed from the 1950s onwards, and then only in a few regions, such as North America. "Do you really need 20 stations in one region to get a monthly temperature figure? The answer is no. Supersaturating your coverage doesn't give you much more bang for your buck," he says. They will, however, help researchers spot short-term regional variations in climate change, something that is likely to be valuable as climate change takes hold.
  • Despite his reservations, Thorne says climate science stands to benefit from Muller's project. "We need groups like Berkeley stepping up to the plate and taking this challenge on, because it's the only way we're going to move forwards. I wish there were 10 other groups doing this," he says.
  • Muller's project is organised under the auspices of Novim, a Santa Barbara-based non-profit organisation that uses science to find answers to the most pressing issues facing society and to publish them "without advocacy or agenda". Funding has come from a variety of places, including the Fund for Innovative Climate and Energy Research (funded by Bill Gates), and the Department of Energy's Lawrence Berkeley Lab. One donor has had some climate bloggers up in arms: the man behind the Charles G Koch Charitable Foundation owns, with his brother David, Koch Industries, a company Greenpeace called a "kingpin of climate science denial". On this point, Muller says the project has taken money from right and left alike.
  • No one who spoke to the Guardian about the Berkeley Earth project believed it would shake the faith of the minority who have set their minds against global warming. "As new kids on the block, I think they will be given a favourable view by people, but I don't think it will fundamentally change people's minds," says Thorne. Brillinger has reservations too. "There are people you are never going to change. They have their beliefs and they're not going to back away from them."
Weiye Loh

Rationally Speaking: Evolution as pseudoscience? - 0 views

rationallyspeaking.blogspot.com/...volution-as-pseudoscience.html

Evolution Pseudo-Science Science Biology Philosophy Dermacation

shared by Weiye Loh on 16 Apr 11 - No Cached
  • I have been intrigued by an essay by my colleague Michael Ruse, entitled “Evolution and the idea of social progress,” published in a collection that I am reviewing, Biology and Ideology from Descartes to Dawkins (gotta love the title!), edited by Denis Alexander and Ronald Numbers.
  • Ruse's essay in the Alexander-Numbers collection questions the received story about the early evolution of evolutionary theory, which sees the stuff that immediately preceded Darwin — from Lamarck to Erasmus Darwin — as protoscience, the immature version of the full fledged science that biology became after Chuck's publication of the Origin of Species. Instead, Ruse thinks that pre-Darwinian evolutionists really engaged in pseudoscience, and that it took a very conscious and precise effort on Darwin’s part to sweep away all the garbage and establish a discipline with empirical and theoretical content analogous to that of the chemistry and physics of the time.
  • Ruse asserts that many serious intellectuals of the late 18th and early 19th century actually thought of evolution as pseudoscience, and he is careful to point out that the term “pseudoscience” had been used at least since 1843 (by the physiologist Francois Magendie)
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  • Ruse’s somewhat surprising yet intriguing claim is that “before Charles Darwin, evolution was an epiphenomenon of the ideology of [social] progress, a pseudoscience and seen as such. Liked by some for that very reason, despised by others for that very reason.”
  • Indeed, the link between evolution and the idea of human social-cultural progress was very strong before Darwin, and was one of the main things Darwin got rid of.
  • The encyclopedist Denis Diderot was typical in this respect: “The Tahitian is at a primary stage in the development of the world, the European is at its old age. The interval separating us is greater than that between the new-born child and the decrepit old man.” Similar nonsensical views can be found in Lamarck, Erasmus, and Chambers, the anonymous author of The Vestiges of the Natural History of Creation, usually considered the last protoscientific book on evolution to precede the Origin.
  • On the other side of the divide were social conservatives like the great anatomist George Cuvier, who rejected the idea of evolution — according to Ruse — not as much on scientific grounds as on political and ideological ones. Indeed, books like Erasmus’ Zoonomia and Chambers’ Vestiges were simply not much better than pseudoscientific treatises on, say, alchemy before the advent of modern chemistry.
  • people were well aware of this sorry situation, so much so that astronomer John Herschel referred to the question of the history of life as “the mystery of mysteries,” a phrase consciously adopted by Darwin in the Origin. Darwin set out to solve that mystery under the influence of three great thinkers: Newton, the above mentioned Herschel, and the philosopher William Whewell (whom Darwin knew and assiduously frequented in his youth)
  • Darwin was a graduate of the University of Cambridge, which had also been Newton’s home. Chuck got drilled early on during his Cambridge education with the idea that good science is about finding mechanisms (vera causa), something like the idea of gravitational attraction underpinning Newtonian mechanics. He reflected that all the talk of evolution up to then — including his grandfather’s — was empty, without a mechanism that could turn the idea into a scientific research program.
  • The second important influence was Herschel’s Preliminary Discourse on the Study of Natural Philosophy, published in 1831 and read by Darwin shortly thereafter, in which Herschel sets out to give his own take on what today we would call the demarcation problem, i.e. what methodology is distinctive of good science. One of Herschel’s points was to stress the usefulness of analogical reasoning
  • Finally, and perhaps most crucially, Darwin also read (twice!) Whewell’s History of the Inductive Sciences, which appeared in 1837. In it, Whewell sets out his notion that good scientific inductive reasoning proceeds by a consilience of ideas, a situation in which multiple independent lines of evidence point to the same conclusion.
  • the first part of the Origin, where Darwin introduces the concept of natural selection by way of analogy with artificial selection can be read as the result of Herschel’s influence (natural selection is the vera causa of evolution)
  • the second part of the book, constituting Darwin's famous “long argument,” applies Whewell’s method of consilience by bringing in evidence from a number of disparate fields, from embryology to paleontology to biogeography.
  • What, then, happened to the strict coupling of the ideas of social and biological progress that had preceded Darwin? While he still believed in the former, the latter was no longer an integral part of evolution, because natural selection makes things “better” only in a relative fashion. There is no meaningful sense in which, say, a large brain is better than very fast legs or sharp claws, as long as you still manage to have dinner and avoid being dinner by the end of the day (or, more precisely, by the time you reproduce).
  • Ruse’s claim that evolution transitioned not from protoscience to science, but from pseudoscience, makes sense to me given the historical and philosophical developments. It wasn’t the first time either. Just think about the already mentioned shift from alchemy to chemistry
  • Of course, the distinction between pseudoscience and protoscience is itself fuzzy, but we do have what I think are clear examples of the latter that cannot reasonably be confused with the former, SETI for one, and arguably Ptolemaic astronomy. We also have pretty obvious instances of pseudoscience (the usual suspects: astrology, ufology, etc.), so the distinction — as long as it is not stretched beyond usefulness — is interesting and defensible.
  • It is amusing to speculate which, if any, of the modern pseudosciences (cryonics, singularitarianism) might turn out to be able to transition in one form or another to actual sciences. To do so, they may need to find their philosophically and scientifically savvy Darwin, and a likely bet — if history teaches us anything — is that, should they succeed in this transition, their mature form will look as different from the original as chemistry and alchemy. Or as Darwinism and pre-Darwinian evolutionism.
  • Darwin called the Origin "one long argument," but I really do think that recognizing that the book contains (at least) two arguments could help to dispel that whole "just a theory" canard. The first half of the book is devoted to demonstrating that natural selection is the true cause of evolution; vera causa arguments require proof that the cause's effect be demonstrated as fact, so the second half of the book is devoted to a demonstration that evolution has really happened. In other words, evolution is a demonstrable fact and natural selection is the theory that explains that fact, just as the motion of the planets is a fact and gravity is a theory that explains it.
  • Cryogenics is the study of the production of low temperatures and the behavior of materials at those temperatures. It is a legitimate branch of physics and has been for a long time. I think you meant 'cryonics'.
  • The Singularity means different things to different people. It is uncharitable to dismiss all "singularitarians" by debunking Kurzweil. He is low hanging fruit. Reach for something higher.
  • Weiye Loh on 16 Apr 11
     
    "before Charles Darwin, evolution was an epiphenomenon of the ideology of [social] progress, a pseudoscience and seen as such. Liked by some for that very reason, despised by others for that very reason."
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