{"id":"W4251853229","doi":"10.1515/iupac.88.1462","title":"Urogenital","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Medical and Health Sciences Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Reproductive system; Computer science; Biology; Linguistics; Philosophy; Data mining","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001188433,0.0002784647,0.0007536547,0.0002274093,0.0003372059,0.00007984275,0.0006353696,0.0004270922,0.01203623],"category_scores_gemma":[0.002770222,0.0001938951,0.000186452,0.0001411061,0.0005796454,0.00005903164,0.0002627714,0.001340119,0.00003365396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002508542,"about_ca_system_score_gemma":0.005019016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003132582,"about_ca_topic_score_gemma":0.0005156979,"domain_scores_codex":[0.9951457,0.00004712998,0.0004252625,0.0005272953,0.003171227,0.0006834276],"domain_scores_gemma":[0.996885,0.00007239103,0.0001904282,0.001204184,0.0005912568,0.001056723],"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":[0.0002100456,0.0002529008,0.00005799458,0.0006537433,0.00004181976,0.0006949274,0.000005761482,3.089234e-8,0.000002021257,0.000001737325,0.9914196,0.006659382],"study_design_scores_gemma":[0.00105637,0.0007379877,0.0003532014,0.0004440941,0.0000951925,0.0001418327,0.00001366098,0.00002421071,0.000003287312,0.00003740731,0.9969148,0.0001779766],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002910021,0.00117824,0.00001086794,0.004703123,0.0007531641,0.0004684908,0.9921681,0.00003347143,0.0003935727],"genre_scores_gemma":[0.00002845679,0.001585085,0.00006640432,0.001149514,0.002017256,0.00001465379,0.9919853,0.00001787022,0.003135456],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01200257,"threshold_uncertainty_score":0.9888669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05717712016054542,"score_gpt":0.5602485312091775,"score_spread":0.5030714110486321,"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."}}