{"id":"W4385274648","doi":"10.1111/geb.13735","title":"BioDeepTime: A database of biodiversity time series for modern and fossil assemblages","year":2023,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Youth Innovation Promotion Association; Natural Environment Research Council; Agentúra na Podporu Výskumu a Vývoja; Chinese Academy of Sciences; Sight Research UK; Deutsche Forschungsgemeinschaft; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; European Commission; Volkswagen Foundation; Smithsonian Institution; European Research Council; Leverhulme Trust; National Science Foundation","keywords":"Quadrat; Assemblage (archaeology); Biodiversity; Taxon; Ecology; Geography; Series (stratigraphy); Sampling (signal processing); Physical geography; Ecosystem; Taxonomic rank; Geology; Biology; Paleontology; Transect","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001863966,0.001109609,0.001168151,0.01260547,0.0003095761,0.002133769,0.001745391,0.0009307536,0.01858171],"category_scores_gemma":[0.012739,0.000658791,0.0007632599,0.01540018,0.0002648561,0.002878586,0.001614078,0.0008992789,0.01246625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000662897,"about_ca_system_score_gemma":0.001189917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004977601,"about_ca_topic_score_gemma":0.005805143,"domain_scores_codex":[0.9984043,0.0001567446,0.000410969,0.0004971233,0.0004354902,0.00009541141],"domain_scores_gemma":[0.9927019,0.002009043,0.00210811,0.001448402,0.00122031,0.0005122579],"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":[0.001431121,0.0002917755,0.1075667,0.00515735,0.0007939739,0.0007872187,0.0006520894,0.01621148,0.005945328,0.009520708,0.5767507,0.2748916],"study_design_scores_gemma":[0.0002458655,0.0002025291,0.2138933,0.0008117373,0.0001907388,0.0008178534,0.0003836922,0.02175977,0.005380454,0.008582364,0.7474572,0.0002745969],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008041075,0.0003168387,0.005797132,0.00006147343,0.00003237616,0.00008397087,0.9792926,0.004537858,0.001836658],"genre_scores_gemma":[0.01470112,0.0002819037,0.01258186,0.00003441083,0.00002624345,0.0003707386,0.9710177,0.0004256563,0.0005603303],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01858171,"threshold_uncertainty_score":0.06216204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286649917542318,"score_gpt":0.2302683348398755,"score_spread":0.2174018356644523,"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."}}