An anonymous reader quotes a report from the Guardian: The critical Atlantic current system appears significantly more likely to collapse than previously thought after new research found that climate models predicting the biggest slowdown are the most realistic. Scientists called the new finding "very concerning" as a collapse would have catastrophic consequences for Europe, Africa and the Americas. The Atlantic meridional overturning circulation (Amoc) is a major part of the global climate system and was already known to be at its weakest for 1,600 years as a result of the climate crisis. Scientists spotted warning signs of a tipping point in 2021 and know that the Amoc has collapsed in the Earth's past.

Climate scientists use dozens of different computer models to assess the future climate. However, for the complex Amoc system, these produce widely varying results, ranging from some that indicate no further slowdown by 2100 to those suggesting a huge deceleration of about 65%, even when carbon emissions from fossil fuel burning are gradually cut to net zero. The research combined real-world ocean observations with the models to determine the most reliable, and this hugely reduced the spread of uncertainty. They found an estimated slowdown of 42% to 58% in 2100, a level almost certain to end in collapse.

The Amoc is a major part of the global climate system and brings sun-warmed tropical water to Europe and the Arctic, where it cools and sinks to form a deep return current. A collapse would shift the tropical rainfall belt on which many millions of people rely to grow their food, plunge western Europe into extreme cold winters and summer droughts, and add 50-100cm to already rising sea levels around the Atlantic. The slowdown has to do with the Arctic's rapidly rising temperatures from global warming. "Warmer water is less dense and therefore sinks into the depths more slowly," explains the Guardian. "This slowing allows more rainfall to accumulate in the salty surface waters, also making it less dense, and further slowing the sinking and forming an Amoc feedback loop."

The new research has been published in the journal Science Advances.

An anonymous reader quotes a report from Ars Technica: On Thursday, OpenAI announced it had developed a large language model specifically trained on common biology workflows. Called GPT-Rosalind after Rosalind Franklin, the model appears to differ from most science-focused models from major tech companies, which have generally taken a more generic approach that works for various fields. In a press briefing, Yunyun Wang, OpenAI's Life Sciences Product Lead, said the system was designed to tackle two major roadblocks faced by current biology researchers. One is the massive datasets created by decades of genome sequencing and protein biochemistry, which can be too much for any one researcher to take in. The second is that biology has many highly specialized subfields, each with its own techniques and jargon. So, for example, a geneticist who finds themselves working on a gene that's active in brain cells might struggle to understand the immense neurobiological literature.

Wang said the company had taken an LLM and trained it on 50 of the most common biological workflows, as well as on how to access the major public databases of biological information. Further training has resulted in a system that can suggest likely biological pathways and prioritize potential drug targets. "We're connecting genotype to phenotype through known pathways and regulatory mechanisms, infer likely structural or functional properties of proteins, and really leveraging this mechanistic understanding," Wang said. To address LLMs' tendencies toward sycophancy and overenthusiasm, OpenAI says it has tuned the model to be more skeptical, so it's more likely to tell you when something is a bad drug target. There was a lot of talk about GPT-Rosalind's "reasoning" and "expert-level" abilities. We were told that the former was defined as being able to work through complex, multi-step processes, while the latter was derived from the model's performance on a handful of benchmarks. Access to GPT-Rosalind is currently limited "due to concerns about the model's potential for harmful outputs if asked to do something like optimize a virus's infectivity," notes Ars. Only U.S.-based organizations can request access at the moment.