{"id":"W4393889122","doi":"10.5281/zenodo.8154672","title":"BioDeepTime: database and compilation code","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Database; Computer science; Code (set theory); Programming language","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":[0.003370905,0.002226157,0.002173402,0.005437266,0.001600989,0.007859978,0.005792748,0.001450168,0.2739739],"category_scores_gemma":[0.01320227,0.003407901,0.002274643,0.007160958,0.0009130477,0.005417084,0.004166341,0.004000827,0.3964834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002577102,"about_ca_system_score_gemma":0.00411335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006380169,"about_ca_topic_score_gemma":0.004148266,"domain_scores_codex":[0.9971533,0.0002874592,0.000458981,0.0007596522,0.001075861,0.0002647521],"domain_scores_gemma":[0.9932629,0.001222621,0.0004611906,0.001954452,0.002607132,0.0004916798],"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":[0.0002297366,0.0000308981,0.0003800678,0.0005149624,0.00003773027,0.00008391376,0.00008328654,0.0002691914,0.002005992,0.001836903,0.9669669,0.02756032],"study_design_scores_gemma":[0.00008758091,0.0000260796,0.001057628,0.0002042151,0.00003203385,0.0002690811,0.00007753616,0.0008999917,0.006737744,0.003035151,0.9874786,0.00009423662],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009948898,0.0005163163,0.05371333,0.0007139806,0.0007623629,0.0004397834,0.4925767,0.4150455,0.03523728],"genre_scores_gemma":[0.003877749,0.0006322543,0.05942698,0.0008530563,0.0001744839,0.00112533,0.7100576,0.196332,0.02752064],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2739739,"threshold_uncertainty_score":0.9165341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03806320532596763,"score_gpt":0.280869321928475,"score_spread":0.2428061166025074,"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."}}