{"id":"W3037249913","doi":"10.1038/s41597-020-0551-2","title":"The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities","year":2020,"lang":"en","type":"article","venue":"Scientific Data","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; Memorial University of Newfoundland; Université Laval; ArcticNet; St. Francis Xavier University; Université du Québec à Chicoutimi","funders":"Executive Agency for Small and Medium-sized Enterprises; Universität Rostock; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; European Commission; ArcticNet","keywords":"Benthic zone; Fauna; Biomass (ecology); Database; Environmental science; Bioturbation; Oceanography; Ecosystem; Benthos; Ecology; Macrobenthos; Abundance (ecology); Biology; Geology; Computer science; Sediment; Paleontology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001839119,0.000106155,0.000122607,0.00002473596,0.001264011,0.0004533095,0.000934479,0.00005295577,0.0002513659],"category_scores_gemma":[0.0006504908,0.00006908938,0.00001278912,0.0002193515,0.001253091,0.0004185943,0.0007779934,0.0001056664,0.00007542834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001829489,"about_ca_system_score_gemma":0.00009852548,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005256028,"about_ca_topic_score_gemma":0.01810096,"domain_scores_codex":[0.9984482,0.0001046149,0.0001507314,0.0007262433,0.0001721342,0.0003981074],"domain_scores_gemma":[0.9985661,0.0004076658,0.00004234557,0.0007231332,0.00004264548,0.0002181192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002602361,0.00001131518,0.664741,0.0001199196,0.00006793273,0.00002099443,0.000105828,7.802049e-7,0.0001940029,0.000619585,0.3079191,0.02593928],"study_design_scores_gemma":[0.0007481055,0.0001606149,0.3260866,0.00001379475,0.00006502331,0.0000623609,0.0007627382,0.06931352,0.0001160629,0.001300266,0.6010686,0.0003023082],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8425311,0.008211646,0.0002644629,0.01824419,0.001808471,0.0009690327,0.1269245,0.00007853642,0.0009680092],"genre_scores_gemma":[0.9474111,0.0003700801,0.00159344,0.0007600136,0.0002176495,0.000005594343,0.0486278,0.000004096813,0.001010223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3386545,"threshold_uncertainty_score":0.9998161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08922836685675936,"score_gpt":0.297443035589597,"score_spread":0.2082146687328376,"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."}}