{"id":"W1964255443","doi":"10.1371/journal.pbio.1002033","title":"Finding Our Way through Phenotypes","year":2015,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":222,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Simon Fraser University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute; National Human Genome Research Institute; Biotechnology and Biological Sciences Research Council; National Science Foundation","keywords":"Phenomics; Biology; Bottleneck; Data science; Genomics; Systematics; Phenotype; Systems biology; Computational biology; Ecology; Computer science; Genome; Genetics; Taxonomy (biology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001383259,0.0001235264,0.0001618008,0.00002156465,0.00004630972,0.000008361016,0.0002196395,0.0002942167,0.00001198167],"category_scores_gemma":[0.0004777483,0.00009528291,0.00004877794,0.00006113372,0.0001055756,0.000001591277,0.0001443956,0.00009594583,0.0001067367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001024948,"about_ca_system_score_gemma":0.00005319464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002145811,"about_ca_topic_score_gemma":0.00001248531,"domain_scores_codex":[0.9991294,0.00007574959,0.0001407748,0.0003075578,0.00006108861,0.0002854364],"domain_scores_gemma":[0.9995541,0.00001359636,0.00005057352,0.0002396365,0.00005380858,0.00008828392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003057138,0.0003541019,0.03352012,0.00003702796,0.0003830537,0.00001938115,0.0008032014,0.000009528931,0.8089513,0.003346874,0.09907591,0.05319377],"study_design_scores_gemma":[0.001346867,0.00159143,0.00115725,0.0000207839,0.00004147428,0.00003858886,0.001302251,0.00005474611,0.2133289,0.005977644,0.77464,0.0005001145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787977,0.003025367,0.00364463,0.002355387,0.000772146,0.0001167374,0.0000251152,0.00009545209,0.01116749],"genre_scores_gemma":[0.9916388,0.00007425081,0.006161585,0.0006709659,0.000541234,0.00001423407,0.0001035941,0.00001295686,0.0007823586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6755641,"threshold_uncertainty_score":0.3885524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09074615135526791,"score_gpt":0.3319444958987222,"score_spread":0.2411983445434543,"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."}}