{"id":"W2966585420","doi":"10.1111/geb.12975","title":"sFDvent: A global trait database for deep‐sea hydrothermal‐vent fauna","year":2019,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Fisheries and Oceans Canada; University of Victoria","funders":"Division of Environmental Biology; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro; Russian Science Foundation; Norges Forskningsråd; Canada Research Chairs; Andrew W. Mellon Foundation; Sight Research UK; Deutsche Forschungsgemeinschaft; Natural Environment Research Council; University of Southampton; Institut Français de Recherche pour l'Exploitation de la Mer","keywords":"Biodiversity; Ecosystem; Ecology; Database; Trait; Chemosynthesis; Hydrothermal vent; Trophic level; Biology; Habitat; Abundance (ecology); Fauna; Taxon; Geography; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004649475,0.0002054025,0.0002987226,0.00007493779,0.000270642,0.00002591992,0.0003090386,0.0003274707,0.004244197],"category_scores_gemma":[0.00003447252,0.0001673966,0.0001660536,0.000351291,0.0003898114,0.0001190007,0.00005725083,0.000137059,0.0002629456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008143092,"about_ca_system_score_gemma":0.00005631293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065095,"about_ca_topic_score_gemma":0.01740511,"domain_scores_codex":[0.9982681,0.0001329309,0.0002283648,0.0005371849,0.0001006331,0.0007328466],"domain_scores_gemma":[0.9993109,0.0001442876,0.00006958049,0.0002150209,0.00004081198,0.0002193709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004180495,0.00006288091,0.9843683,0.00003756644,0.0001264715,0.00001426277,0.000005591481,0.00001434113,0.000002587804,0.001627974,0.0007370764,0.01258487],"study_design_scores_gemma":[0.001048708,0.0008674293,0.9782826,0.000004322978,0.00003531526,0.00007433827,0.00003608748,0.001009111,0.000004574159,0.004940509,0.01349867,0.000198346],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850882,0.001222978,0.00002560488,0.0007103657,0.0006414279,0.0007435554,0.002411235,0.00004968787,0.009106953],"genre_scores_gemma":[0.997214,0.0001424206,0.0003051661,0.001180288,0.00007663748,0.00001646731,0.001000644,0.000002046883,0.00006238063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01634002,"threshold_uncertainty_score":0.9966661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008516388456451012,"score_gpt":0.234832730402115,"score_spread":0.226316341945664,"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."}}