Science Biology Without dinosaurs, there'd be no Thanksgiving dinner The evolution of today's beloved side dishes took off 66 million years ago. Breakthroughs, discoveries, and DIY tips sent every weekday. It's hard to pick a favorite dish on your Thanksgiving plate. But regardless of your selection, there's a decent chance its history can be traced back to one of the most cataclysmic events in Earth's history. "The dinosaurs' absence meant changes in the forest structure-you went from a more open canopy to a more-closed canopy rainforest," explained Mike Donovan, a paleobotanist and the fossil plants collections manager at Chicago's Field Museum .

Survival International's new report illustrates the dangers they face--and their resilience. This photo of an Awa Guajá couple was taken only five days before their first contact with outsiders in 1992. Breakthroughs, discoveries, and DIY tips sent every weekday. Half of the world's remaining uncontacted Indigenous groups may disappear within a decade without concerted conservation efforts . The dire assessment is detailed in a new report published on October 27 by the nonprofit advocacy group Survival International, and is based on years of field research, interviews, and information gathering expeditions.

Bennett's tree kangaroos, one of Australia's most mysterious marsupials, have long eluded researchers. Our new study, published in Australian Mammalogy today, has achieved a breakthrough: using thermal drones to detect these rare animals with unprecedented efficiency. Tree kangaroos are found only in the tropical rainforests of Australia and New Guinea. Unlike their ground-dwelling relatives, they spend their lives in treetops, feeding on leaves and vines. Their dependence on rainforest trees makes them vulnerable to deforestation and climate change.

The fallout from the OpenAI board's failed attempt to fire CEO Sam Altman last November took an unexpected turn on Thursday, in events that could have a significant bearing on the future of the company and the wider world of artificial intelligence. Elon Musk filed a lawsuit against OpenAI in a San Francisco court, alleging that Altman and co-founder Greg Brockman have violated OpenAI's founding mission to develop AI safely and for the benefit of humanity. The billionaire owner of SpaceX and X (formerly Twitter) co-founded OpenAI alongside Altman and Brockman back in 2015, but stepped away from the company in 2018. Musk disagreed with Altman and Brockman's plan to turn OpenAI from a non-profit to a for-profit company, and before stepping down, reportedly mounted an unsuccessful bid to install himself as CEO. Musk is suing Altman, Brockman, and several of OpenAI's business entities for breach of contract, breach of fiduciary duty, and unfair business practices, seeking unspecified damages above 105,000.

This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed by authoritative sources like the IPCC reports and peer-reviewed scientific literature, Climinator employs an innovative Mediator-Advocate framework. This design allows Climinator to effectively synthesize varying scientific perspectives, leading to robust, evidence-based evaluations. Our model demonstrates remarkable accuracy when testing claims collected from Climate Feedback and Skeptical Science. Notably, when integrating an advocate with a climate science denial perspective in our framework, Climinator's iterative debate process reliably converges towards scientific consensus, underscoring its adeptness at reconciling diverse viewpoints into science-based, factual conclusions. While our research is subject to certain limitations and necessitates careful interpretation, our approach holds significant potential. We hope to stimulate further research and encourage exploring its applicability in other contexts, including political fact-checking and legal domains.

There's much, much more to the rainforest than meets the eye. Even a highly trained observer can struggle to pick out individual animals in the tangle of plant life--animals that are often specifically adapted to hide from their enemies. Listen to the music of the forest, though, and you can get a decent idea of the species by their chirps, croaks, and grunts. This is why scientists are increasingly bugging rainforests with microphones--a burgeoning field known as bioacoustics--and using AI to automatically parse sounds to identify species. Writing today in the journal Nature Communications, researchers describe a proof-of-concept project in the lowland Chocó region of Ecuador that shows the potential power of bioacoustics in conserving forests.

A millennium of endeavors to fully recognize and foresee the evolution of dynamic environments has produced many mathematical models for forecasting, and information-gathering techniques, but also exceptionally complicated computational systems. Predefined complicated realities called hyperchaotic frameworks [1] demonstrate unpredictable sequences of behavior over time and sometimes defy standards. These events' temporal and spatial relationships can be compared to physiological kinetics [2]. Several complicated frameworks are currently developed to comprehend spontaneous incidents, their erratic conduct, and how changing the circumstances of actual events may result in an unanticipated shift in the result. Over the duration of the past couple of eons, the objective of being able to understand and anticipate unpredictable actions has been accomplished with the aid of innovations in technology [3] and fundamental principles [4].

The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosystems by leveraging the vast amount of remote sensing data that is continuously being collected. The primary objective of this workshop is to bring together the Earth and environmental science communities as well as the multimodal representation learning communities to explore new ways of harnessing technological advancements in support of environmental monitoring. The MultiEarth Workshop also seeks to provide a common benchmark for processing multimodal remote sensing information by organizing public challenges focused on monitoring the Amazon rainforest. These challenges include estimating deforestation, detecting forest fires, translating synthetic aperture radar (SAR) images to the visible domain, and projecting environmental trends. This paper presents the challenge guidelines, datasets, and evaluation metrics. Our challenge website is available at https://sites.google.com/view/rainforest-challenge/multiearth-2023.

The widespread usage of computer-based assessments and individualized learning platforms has resulted in an increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduce reliance on human subject experts at each step of the process. AIG has been used in test development for some time. Still, the use of machine learning algorithms has introduced the potential to improve the efficiency and effectiveness of the process greatly. The approach presented in this paper utilizes OpenAI's latest transformer-based language model, GPT-3, to generate reading passages. Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage. For each prompt, we generated multiple passages, the final passage was selected according to the Lexile score agreement with the original passage. In the final round, the selected passage went through a simple revision by a human editor to ensure the text was free of any grammatical and factual errors. All AI-generated passages, along with original passages were evaluated by human judges according to their coherence, appropriateness to fourth graders, and readability.

Ecologists are increasingly using traces of genetic material left behind by living organisms left behind in the environment, called environmental DNA (eDNA), to catalogue and monitor biodiversity. Based on these DNA traces, researchers can determine which species are present in a certain area. Obtaining samples from water or soil is easy, but other habitats – such as the forest canopy – are difficult for researchers to access. As a result, many species remain untracked in poorly explored areas. Researchers at ETH Zurich and the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, and the company SPYGEN have partnered to develop a special drone that can autonomously collect samples on tree branches.