{"id":"W4393749534","doi":"10.5281/zenodo.7884472","title":"Functional traits database for North American birds","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Database; Geography; Computer science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004227298,0.001433962,0.0009610705,0.003280232,0.0006120445,0.001132682,0.001661938,0.0009960314,0.02726124],"category_scores_gemma":[0.001841764,0.0004870651,0.0007151394,0.006025729,0.0002315212,0.000745187,0.001058707,0.0009791912,0.02677267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009994222,"about_ca_system_score_gemma":0.001535985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04410721,"about_ca_topic_score_gemma":0.09255526,"domain_scores_codex":[0.9995732,0.0000438606,0.00006147077,0.0001766487,0.00008838244,0.00005639062],"domain_scores_gemma":[0.9990904,0.0001894846,0.0001726741,0.0001629944,0.0002414859,0.0001429079],"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":[0.0002068661,0.00005489812,0.01152763,0.001626004,0.0001335933,0.0001169418,0.0001052741,0.0007961084,0.0006913326,0.001067775,0.9713935,0.01228009],"study_design_scores_gemma":[0.0001676339,0.00002154194,0.0538195,0.0003223672,0.00006727829,0.0001769831,0.0001286888,0.001044956,0.0004424132,0.00112804,0.9426398,0.00004074623],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005973071,0.00007912677,0.0001220053,0.00002138377,0.000005539256,0.000007588166,0.9984533,0.0001802652,0.0005334565],"genre_scores_gemma":[0.0008322445,0.00004608536,0.0004065983,0.00001857526,0.000002102629,0.00004984151,0.9983464,0.00002602413,0.0002720531],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04410721,"threshold_uncertainty_score":0.09119791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03450920420540812,"score_gpt":0.2552032641782554,"score_spread":0.2206940599728473,"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."}}