{"id":"W4238447839","doi":"10.1515/iupac.88.0458","title":"Androgen","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001321237,0.001235978,0.001102565,0.003306672,0.0007830607,0.002999344,0.001923144,0.001333462,0.1743421],"category_scores_gemma":[0.01118658,0.0005542212,0.001564336,0.005655407,0.0003405886,0.002353829,0.002177168,0.001543809,0.1918574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141343,"about_ca_system_score_gemma":0.002556264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01535834,"about_ca_topic_score_gemma":0.02782991,"domain_scores_codex":[0.9980192,0.0003381951,0.0003887825,0.0006465023,0.0004066397,0.0002006201],"domain_scores_gemma":[0.9958794,0.001044176,0.0004454204,0.00104916,0.001330953,0.0002508963],"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":[0.00009728566,0.00001593354,0.001610182,0.001256868,0.00003856056,0.00002315606,0.00002933096,0.0001373228,0.0001201846,0.001048557,0.9852384,0.01038412],"study_design_scores_gemma":[0.00007593945,0.00001174849,0.003207718,0.000553235,0.00002329397,0.00005210464,0.00005842788,0.0001010176,0.0001432011,0.001131341,0.9946239,0.00001805939],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001415658,0.0002037928,0.000155944,0.0001376236,0.00007136677,0.00003264615,0.9952742,0.0002935227,0.003689293],"genre_scores_gemma":[0.0005012543,0.0002096369,0.0003779313,0.0002089589,0.00002083874,0.0001165188,0.9960627,0.00008420275,0.002417959],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1743421,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01232087993629025,"score_gpt":0.4533617960639906,"score_spread":0.4410409161277004,"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."}}