{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002685766,0.001561476,0.001263681,0.01377398,0.0006759565,0.002344425,0.002227158,0.001455841,0.01205629],"category_scores_gemma":[0.01410955,0.0006591094,0.001113325,0.01164976,0.0004266688,0.003460262,0.003842372,0.001017663,0.007791282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009257297,"about_ca_system_score_gemma":0.001903614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007177293,"about_ca_topic_score_gemma":0.008477604,"domain_scores_codex":[0.9981484,0.0002228288,0.0004891347,0.0005308469,0.0004510852,0.0001576213],"domain_scores_gemma":[0.9920846,0.002583628,0.001525287,0.001427812,0.001546441,0.0008322289],"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":[0.001626773,0.0003194621,0.1684113,0.009982525,0.0008445748,0.001067225,0.002048353,0.006882325,0.01497287,0.01213897,0.6050466,0.176659],"study_design_scores_gemma":[0.0003398241,0.0001790093,0.2276388,0.0009585548,0.0002886363,0.0008064907,0.0009376796,0.01001328,0.007056884,0.008767146,0.7426962,0.0003175954],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01674282,0.0004317647,0.007693261,0.0001039064,0.00004343891,0.0001268522,0.9655912,0.006632576,0.00263436],"genre_scores_gemma":[0.01967959,0.0001617088,0.01390081,0.00006073637,0.0000175186,0.0003139029,0.9645935,0.0006835051,0.0005886768],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01377398,"threshold_uncertainty_score":0.04033232,"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."}}