{"id":"W4394767091","doi":"10.32942/x2z022","title":"MetaR, a global database on metabolic rates of ectotherms","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"Fonds de recherche du Québec – Nature et technologies; Universidade de São Paulo; University of North Carolina at Chapel Hill; Universidad San Francisco de Quito; Université de Rennes 1; King Abdullah University of Science and Technology; Akademie Věd České Republiky; Bundesministerium für Bildung und Forschung; Smithsonian Tropical Research Institute; Université du Québec à Rimouski; Alexander von Humboldt-Stiftung; Natural Sciences and Engineering Research Council of Canada; Smithsonian Institution","keywords":"Ectotherm; Intertidal zone; Ecology; Biodiversity; Organism; Metabolic rate; Biology; Ecosystem; Invertebrate; Global change; Trait; Taxon; Environmental change; Computer science; Climate change","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008250449,0.001509087,0.0013822,0.006760293,0.0002469728,0.001313275,0.001056595,0.001017462,0.005648456],"category_scores_gemma":[0.004835175,0.0005010711,0.0008979214,0.007975222,0.0002152509,0.001513971,0.001269989,0.0008077152,0.006409364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002967137,"about_ca_system_score_gemma":0.0008911471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002287319,"about_ca_topic_score_gemma":0.003165024,"domain_scores_codex":[0.9991289,0.00008089678,0.0002160278,0.0003402304,0.000174435,0.0000594319],"domain_scores_gemma":[0.9974182,0.0006218327,0.0008782311,0.0005487735,0.000327572,0.0002054383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003413145,0.000242945,0.2166211,0.01964842,0.002654826,0.001528084,0.001513564,0.01274745,0.04184434,0.005762418,0.3810505,0.3129733],"study_design_scores_gemma":[0.0001999108,0.0001842786,0.2953007,0.0009041383,0.0007075818,0.001612384,0.0002446122,0.00411328,0.007516677,0.004061234,0.6849312,0.0002240754],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02250297,0.003283191,0.003703466,0.00006903124,0.00005487356,0.00002960658,0.9642991,0.003588138,0.002469567],"genre_scores_gemma":[0.02505922,0.001596763,0.006756977,0.00006241071,0.00004427181,0.0001076601,0.9653187,0.0004466021,0.0006073504],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.006760293,"threshold_uncertainty_score":0.01889598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03791704595252223,"score_gpt":0.293685876117166,"score_spread":0.2557688301646438,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}