{"id":"W4233750564","doi":"10.1515/iupac.88.1197","title":"Phytoestrogen","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Phytoestrogen effects and 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); Computer science; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006801222,0.0005232606,0.001139424,0.0002752161,0.0003021446,0.0001253945,0.0006076231,0.0005697702,0.002087574],"category_scores_gemma":[0.001011091,0.0003858208,0.0004402873,0.0001167639,0.0003591803,0.00007424271,0.0003032383,0.001392123,0.0000216706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003871205,"about_ca_system_score_gemma":0.003508778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001867194,"about_ca_topic_score_gemma":0.0002341159,"domain_scores_codex":[0.9962423,0.00007900548,0.0003897732,0.0006564896,0.001888082,0.0007443302],"domain_scores_gemma":[0.9963218,0.00006470707,0.000277354,0.002141501,0.0006472417,0.000547405],"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":[0.0004459347,0.0005983855,0.00009287991,0.0007114316,0.000396224,0.001496569,0.000003124187,3.303041e-7,0.00008205631,0.000002050555,0.9922806,0.003890421],"study_design_scores_gemma":[0.001864973,0.001080282,0.000413928,0.0008302296,0.0004363424,0.000263019,0.000006795462,0.000009072231,0.0001948301,0.00004852877,0.9944995,0.0003524704],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001079664,0.002153568,0.000006630422,0.001056802,0.0006415215,0.0007727864,0.9939767,0.00006854927,0.0002437841],"genre_scores_gemma":[0.0001466181,0.001142478,0.00008568797,0.0002632659,0.002673003,0.00003067695,0.9942668,0.00006786799,0.001323569],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.003537951,"threshold_uncertainty_score":0.9998594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789842662154125,"score_gpt":0.4965921950548971,"score_spread":0.4686937684333559,"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."}}