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How Dinosaurs Shaped Fruit Evolution

How Dinosaurs Shaped Fruit Evolution

Yahoo19-05-2025
What do humans have in common with the dinosaurs that trampled through ancient forests? It turns out that both may have a surprising impact on the size of seeds in the fruits growing around them. When researchers mapped the evolution of seed sizes onto that of land animals, they observed that when land animals got bigger, so did fruit seeds—with a few outsize exceptions. A recent study in Palaeontology illustrates how, over the course of natural history, gigantic megafauna such as dinosaurs curbed the growth of seed sizes by physically altering the ecosystem, influencing forest light levels. Today that role may be filled by a much tinier species: humans.
The idea that land animals can alter their environments is 'fairly straightforward and well substantiated in a variety of scales,' explains Clive G. Jones, an ecologist at the Cary Institute of Ecosystem Studies in Millbrook, N.Y., who was not involved in the new study. For instance, savanna elephants push down trees and tear at shrubs, transforming the plant landscape. But even this elephantine influence is minor in comparison to that of prehistoric creatures.
The researchers' new model suggests dinosaurs caused a level of destruction that suppressed an evolutionary tendency for seeds to grow bigger, says study lead author Christopher E. Doughty, an earth system scientist at Northern Arizona University. Bigger seeds tend to attract bigger animals for dispersal and to sprout taller plants, Doughty explains; both factors can give plants better access to sunlight in crowded conditions. But this was generally not the case when there were 'big lumbering dinosaurs knocking things down, opening up the environment' and thinning forests, Doughty says.
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After dinosaurs went extinct, forest understories became about 20 percent darker. This change 'reset the slate' for plants and other animals, Doughty says. And 'during this time the canopy became more closed,' notes Brian Atkinson, a University of Kansas paleobotanist not involved in the study. This growth would have placed evolutionary pressure on seeds to get larger again, Atkinson says, which is also reflected in fossil data. Another dip in seed size occurred with the emergence of early giant mammals and persisted until they died out.
But even though we're far from megafauna-sized, humans' influence on forests—particularly via logging—resembles that of those long-extinct giants, Doughty says. If we continue at this rate, our effect on fruit seeds might someday rival that of dinosaurs.
Jones notes that humans influence plant life in many other ways as well. 'Agriculture [is] one obvious example,' he says, along with 'introducing exotic species, clearing forests to make suburbia, to make cities, and so on.'
That complexity is one reason it could be difficult for the model to predict future fruit seed sizes, Doughty says. Another important factor to consider is the rapid pace at which human technology tends to develop in realms such as farming. Although the model provides a good analytical comparison of forest density alterations by megafauna and by humans, developments such as agriculture mean 'normal ecological rules don't really apply anymore.'
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How China Made an Antarctic Station Run on Majority Clean Energy
How China Made an Antarctic Station Run on Majority Clean Energy

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How China Made an Antarctic Station Run on Majority Clean Energy

Solar panels, wind turbines, a hydrogen energy system and lithium-ion batteries are powering China's newest polar research station Five years ago electrical engineer Sun Hongbin was given what many would consider an impossible task: build a full-fledged clean-energy system amid some of the coldest temperatures on Earth, screaming winds and half-year darkness. China was then building its fifth Antarctic research station, called Qinling, on Inexpressible Island in Terra Nova Bay. And the nation's government was pushing the concept of 'green expeditions' to protect Antarctica's uniquely fragile environment while studying and surveying the continent. 'So having a system that would provide the bulk of Qinling's energy with renewable power fit that goal,' Sun says. But conventional solar and wind installations are no match for temperatures that plummet below –40 degrees Celsius, winds of up to 300 kilometers per hour (kmh) and ferocious blizzards. Such conditions can snap wind turbine blades, sharply reduce the performance of solar panels, and prevent batteries from charging and discharging properly. And of course, there are the six months of polar night, when the sun never rises above the horizon. [Sign up for Today in Science, a free daily newsletter] 'It was a huge challenge' to build a system for the Earth's coldest, darkest and most remote continent, says Sun, now president of Taiyuan University of Technology in China and chief scientist for polar clean energy at the Polar Research Institute of China. But in late 2024 his team traveled to the station to install a system that took $14 million to develop. It consists of 10 wind turbines, 26 solar modules, a hydrogen energy system, a container full of frost-resistant lithium-ion batteries and a smart grid that can predict and balance supply and demand. The entire renewable system is now running and, according to Sun, should provide half of the base's average annual energy needs. 'The use of clean energy is a huge advancement to keep the continent clean,' says Kim Yeadong, chair of the Korean National Committee on Polar Research in South Korea, who was not involved with the project. 'Other stations will probably have to learn how they achieve that much clean energy. I think it's remarkable.' Where Diesel Power Is King A 2024 preprint analysis of 81 Antarctic research bases found that 37 had installed renewable-energy sources such as solar panels and wind turbines. But the proportion of renewable energy these bases used was 'often low,' the researchers wrote. An exception so far has been Belgium's Princess Elisabeth Station, which is only staffed during the Antarctic summer. It runs completely on wind and solar power, taking advantage of the almost 24-hour daylight. 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'Sixty percent is a great start, but one needs to ramp up,' Kammen says. 'The goal really needs to be 100 percent renewable energy all year-round.' It's Time to Stand Up for Science Before you close the page, we need to ask for your support. Scientific American has served as an advocate for science and industry for 180 years, and we think right now is the most critical moment in that two-century history. We're not asking for charity. If you become a Digital, Print or Unlimited subscriber to Scientific American, you can help ensure that our coverage is centered on meaningful research and discovery; that we have the resources to report on the decisions that threaten labs across the U.S.; and that we support both future and working scientists at a time when the value of science itself often goes unrecognized. Click here to subscribe. Solve the daily Crossword

Mathematicians Question AI Performance at International Math Olympiad
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Mathematicians Question AI Performance at International Math Olympiad

AI models supposedly did well on International Math Olympiad problems, but how they got their answers reminds us why we still need people doing math A defining memory from my senior year of high school was a nine-hour math exam with just six questions. Six of the top scorers won slots on the U.S. team for the International Math Olympiad (IMO), the world's longest running math competition for high school students. I didn't make the cut, but became a tenured mathematics professor anyway. This year's olympiad, held last month on Australia's Sunshine Coast, had an unusual sideshow. While 110 students from around the world went to work on complex math problems using pen and paper, several AI companies quietly tested new models in development on a computerized approximation of the exam. Right after the closing ceremonies, OpenAI and later Google DeepMind announced that their models earned (unofficial) gold medals for solving five of the six problems. Researchers like Sébastien Bubeck of OpenAI celebrated these models' successes as a 'moon landing moment' by industry. But are they? Is AI going to replace professional mathematicians? I'm still waiting for the proof. [Sign up for Today in Science, a free daily newsletter] The hype around this year's AI results is easy to understand because the olympiad is hard. To wit, in my senior year of high school, I set aside calculus and linear algebra to focus on olympiad-style problems, which were more of a challenge. Plus the cutting-edge models still in development did so much better at the exam than the commercial models already out there. In a parallel contest administered by Gemini 2.5 pro, Grok 4, o3 high, o4-mini high and DeepSeek R1 all failed to produce a single completely correct solution. It shows that AI models are getting smarter, their reasoning capabilities improving rather dramatically. Yet I'm still not worried. The latest models just got a good grade on a single test—as did many of the students—and a head-to-head comparison isn't entirely fair. The models often employ a 'best-of-n' strategy, generating multiple solutions and then grading themselves to select the strongest. This is akin to having several students work independently, then get together to pick the best solution and submit only that one. If the human contestants were allowed this option, their scores would likely improve too. Other mathematicians are similarly cautioning against the hype. IMO gold medalist Terence Tao (currently a mathematician at the University of California, Los Angeles) noted on Mastodon that what AI can do depends on what the testing methodology is. IMO president Gregor Dolinar said that the organization 'cannot validate the methods [used by the AI models], including the amount of compute used or whether there was any human involvement, or whether the results can be reproduced.' Besides, IMO exam questions don't compare to the kinds of questions professional mathematicians try to answer, where it can take nine years, rather than nine hours, to solve a problem at the frontier of mathematical research. As Kevin Buzzard, a mathematics professor at Imperial College London, said in an online forum, 'When I arrived in Cambridge UK as an undergraduate clutching my IMO gold medal I was in no position to help any of the research mathematicians there.' These days, mathematical research can take more than one lifespan to acquire the right expertise. Like many of my colleagues, I've been tempted to try 'vibe proving'—having a math chat with an LLM as one would with a colleague, asking 'Is it true that...' followed by a technical mathematical conjecture. The chatbot often then supplies a clearly articulated argument that, in my experience, tends to be correct when it comes to standard topics but subtly wrong at the cutting edge. For example, every model I've asked has made the same subtle mistake in assuming that the theory of idempotents behaves the same for weak infinite-dimensional categories as it does for ordinary ones, something that human experts (trust me on this) in my field know to be false. I'll never trust an LLM—which at its core is just predicting what text will come next in a string of words, based on what's in its dataset—to provide a mathematical proof that I can't verify myself. The good news is, we do have an automated mechanism for determining whether proofs can be trusted. Relatively recent tools called 'proof assistants' are software programs (they don't use AI) designed to check whether a logical argument proves the stated claim. They are increasingly attracting attention from mathematicians like Tao, Buzzard and myself who want more assurance that our own proofs are correct. And they offer the potential to help democratize mathematics and even improve AI safety. Suppose I received a letter, in unfamiliar handwriting, from Erode, a city in Tamil Nadu, India, purporting to contain a mathematical proof. Maybe its ideas are brilliant, or maybe they're nonsensical. I'd have to spend hours carefully studying every line, making sure the argument flowed step-by-step, before I'd be able to determine whether the conclusions are true or false. But if the mathematical text were written in an appropriate computer syntax instead of natural language, a proof assistant could check the logic for me. A human mathematician, such as I, would then only need to understand the meaning of the technical terms in the theorem statement. In the case of Srinivasa Ramanujan, a generational mathematical genius who did hail from Erode, an expert did take the time to carefully decipher his letter. In 1913 Ramanujan wrote to the British mathematician G. H. Hardy with his ideas. Luckily, Hardy recognized Ramanujan's brilliance and invited him to Cambridge to collaborate, launching the career of one of the all-time mathematical 'greats.' What's interesting is that some of the AI IMO contestants submitted their answers in the language of the Lean computer proof assistant so that the computer program could automatically check for errors in their reasoning. A start-up called Harmonic posted formal proofs generated by their model for five of the six problems, and ByteDance achieved a silver-medal level performance by solving four of the six problems. But the questions had to be written to accommodate the models' language limitations, and they still needed days to figure it out. Still, formal proofs are uniquely trustworthy. While so-called 'reasoning' models are prompted to break problems down into pieces and explain their 'thinking' step by step, the output is as likely to produce an argument that sounds logical but isn't, as to constitute a genuine proof. By contrast, a proof assistant will not accept a proof unless it is fully precise and fully rigorous, justifying every step in its chain-of-thought. In some circumstances, a hand-waving or approximate solution is good enough, but when mathematical accuracy matters, we should demand that AI-generated proofs are formally verifiable. Not every application of generative AI is so black and white, where humans with the right expertise can determine whether the results are correct or incorrect. In life, there is a lot of uncertainty and it's easy to make mistakes. As I learned in high school, one of the best things about math is the fact that you can prove definitively that some ideas are wrong. So I'm happy to have an AI try to solve my personal math problems, but only if the results are formally verifiable. And we aren't quite there, yet. This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American. It's Time to Stand Up for Science Before you close the page, we need to ask for your support. Scientific American has served as an advocate for science and industry for 180 years, and we think right now is the most critical moment in that two-century history. We're not asking for charity. If you become a Digital, Print or Unlimited subscriber to Scientific American, you can help ensure that our coverage is centered on meaningful research and discovery; that we have the resources to report on the decisions that threaten labs across the U.S.; and that we support both future and working scientists at a time when the value of science itself often goes unrecognized. Click here to subscribe. Solve the daily Crossword

Miniature Neutrino Detector Catches Elusive Particles at Nuclear Reactor
Miniature Neutrino Detector Catches Elusive Particles at Nuclear Reactor

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Miniature Neutrino Detector Catches Elusive Particles at Nuclear Reactor

A relatively small detector caught neutrinos from a nuclear reactor using a technique known as coherent scattering Physicists have caught neutrinos from a nuclear reactor using a device weighing just a few kilograms, orders of magnitude less massive than standard neutrino detectors. The technique opens new ways to stress-test the known laws of physics and to detect the copious neutrinos produced in the hearts of collapsing stars. 'They finally did it,' says Kate Scholberg, a physicist at Duke University in Durham, North Carolina. 'And they have very beautiful result.' The experiment, called CONUS+, is described on 30 July in Nature. Challenging quarry Neutrinos are elementary particles that have no electrical charge and generally don't interact with other matter, making them extraordinarily difficult to detect. Most neutrino experiments catch these elusive particles by observing flashes of light that are generated when a neutrino collides with an electron, proton or neutron. These collisions occur extremely infrequently, so such detectors typically have masses of tonnes or thousands of tonnes to provide enough target material to gather neutrinos in relevant numbers. [Sign up for Today in Science, a free daily newsletter] Scholberg and her collaborators first demonstrated the mini-detector technique in 2017, using it to catch neutrinos produced by an accelerator at Oak Ridge National Laboratory in Tennessee. The Oak Ridge particles have slightly higher energies than those made in reactors. As a result, detecting reactor neutrinos was even more challenging, she says. But lower-energy neutrinos also allow for a more precise test of the standard model of physics. Scholberg's COHERENT detector was the first to exploit a phenomenon called coherent scattering, in which a neutrino 'scatters' off an entire atomic nucleus rather than the atom's constituent particles. Coherent scattering uses the fact that particles of matter can act as waves — and the lower the particles' energy, the longer their wavelength, says Christian Buck, a leader of the CONUS collaboration. If the wavelength of a neutrino is similar to the nucleus's diameter, 'then the neutrino sees the nucleus as one thing. It doesn't see the internal structure', says Buck, who is a physicist at the Max Planck Institute for Nuclear Physics in Heidelberg, Germany. The neutrino doesn't interact with any subatomic particles, but does cause the nucleus to recoil — depositing a tiny amount of energy into the detector. Catching sight of a nucleus Coherent scattering occurs more than 100 times as frequently as the interactions used in other detectors, where the neutrino 'sees' a nucleus as a collection of smaller particles with empty space in between. This higher efficiency means that detectors can be smaller and still spot a similar number of particles in the same time frame. 'Now you can afford to build detectors on the kilogram scale,' Buck says. The downside is that the neutrinos deposit much less energy at the nucleus. The recoil induced on a nucleus by a neutrino is comparable to that produced on a ship by a ping-pong ball, Buck says — and has until recent years has been extremely challenging to measure. The CONUS detector is made of four modules of pure germanium, each weighing 1 kilogram. It operated at a nuclear reactor in Germany from 2018 until that reactor was shut down in 2022. The team then moved the detector, upgraded to CONUS+, to the Leibstadt nuclear power plant in Switzerland. From the new location, the team now reports having seen around 395 collision events in 119 days of operation — consistent with the predictions of the standard model of particle physics. After COHERENT's landmark 2017 result, which was obtained with detectors made of caesium iodide, Scholberg's team repeated the feat with detectors made of argon and of germanium. Separately, last year, two experiments originally designed to hunt for dark matter reported seeing hints of low-energy coherent scattering of neutrinos produced by the Sun. Scholberg says that the standard model makes very clean predictions of the rate of coherent scattering and how it changes with different types of atomic nucleus, making it crucial to compare results from as many detecting materials as possible. And if the technique's sensitivity improves further, coherent scattering could help to push forward the state of the art of solar science. Researchers say that coherent scattering will probably not completely replace any existing technologies for detecting neutrinos. But it can spot all three known types of neutrino (and their corresponding antiparticles) down to low energies, whereas some other techniques can capture only one type. This ability means it could complement massive detectors that aim to pick up neutrinos at higher energies, such as the Hyper-Kamiokande observatory now under construction in Japan. This article is reproduced with permission and was first published on July 30 2025. Solve the daily Crossword

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