Assessment of harbour porpoise bycatch along the Portuguese and Galician Coast: insights from strandings over two decades

The Iberian harbour porpoise population is small and fisheries bycatch has been described as one of its most important threats. Data on harbour porpoise strandings collected by the Portuguese and Galician stranding networks between 2000 and 2020 are indicative of a recent mortality increase in the western Iberian coast (particularly in northern Portugal). Overall, in Portugal and Galicia, individuals stranded due to confirmed fishery interaction represented 46.98% of all analysed porpoises, and individuals stranded due to probable fishery interaction represented another 10.99% of all analysed porpoises. Considering the Portuguese annual abundance estimates available between 2011 and 2015, it was possible to calculate that an annual average of 207 individuals was removed from the population in Portuguese waters alone, which largely surpasses the potential biological removal (PBR) estimates (22 porpoises, CI: 12–43) for the same period. These results are conservative and bycatch values from strandings are likely underestimated. A structured action plan accounting for new activities at sea is needed to limit the Iberian porpoise population decline. Meanwhile, there is an urgent need for a fishing effort reorganization to directly decrease porpoise mortality.LA/P/0094/2020; LA/P/0101/2020info:eu-repo/semantics/publishedVersio

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

A palaeoenvironmental reconstruction of the Middle Jurassic of Sardinia (Italy) based on integrated palaeobotanical, palynological and lithofacies data assessment

During the Jurassic, Sardinia was close to continental Europe. Emerged lands started from a single island forming in time a progressively sinking archipelago. This complex palaeogeographic situation gave origin to a diverse landscape with a variety of habitats. Collection- and literature-based palaeobotanical, palynological and lithofacies studies were carried out on the Genna Selole Formation for palaeoenvironmental interpretations. They evidence a generally warm and humid climate, affected occasionally by drier periods. Several distinct ecosystems can be discerned in this climate, including alluvial fans with braided streams (Laconi-Gadoni lithofacies), paralic swamps and coasts (Nurri-Escalaplano lithofacies), and lagoons and shallow marine environments (Ussassai-Perdasdefogu lithofacies). The non-marine environments were covered by extensive lowland and a reduced coastal and tidally influenced environment. Both the river and the upland/hinterland environments are of limited impact for the reconstruction. The difference between the composition of the palynological and palaeobotanical associations evidence the discrepancies obtained using only one of those proxies. The macroremains reflect the local palaeoenvironments better, although subjected to a transport bias (e.g. missing upland elements and delicate organs), whereas the palynomorphs permit to reconstruct the regional palaeoclimate. Considering that the flora of Sardinia is the southernmost of all Middle Jurassic European floras, this multidisciplinary study increases our understanding of the terrestrial environments during that period of time

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License