{"id":"W4249030295","doi":"10.1515/iupac.88.1034","title":"Menstrual Cycle","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Menstrual Health and Disorders","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; Data mining; Philosophy","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":[0.0004845621,0.0004736667,0.001032907,0.000213345,0.0003013796,0.00005577305,0.0003384351,0.0005780417,0.00407449],"category_scores_gemma":[0.0009993459,0.0004036914,0.0002173495,0.00008462553,0.0002507425,0.00006469208,0.000137442,0.000920601,0.00002065317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003158121,"about_ca_system_score_gemma":0.002884908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006468492,"about_ca_topic_score_gemma":0.001134833,"domain_scores_codex":[0.9967951,0.00004706665,0.0005574172,0.0005741632,0.001344883,0.0006813857],"domain_scores_gemma":[0.996847,0.00004970073,0.000382543,0.001588631,0.0004219764,0.0007101848],"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":[0.0006459995,0.0003064262,0.00005134862,0.0009489011,0.00008674779,0.0002889729,0.00001246166,3.555266e-7,9.917861e-7,0.000003993424,0.9892215,0.008432303],"study_design_scores_gemma":[0.00316787,0.0007682543,0.0002462885,0.0005916447,0.0002851794,0.0001294635,0.00005958665,0.00001139076,0.000003723254,0.00009669764,0.994294,0.0003458578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001408343,0.00116557,0.000007493626,0.001299304,0.001257929,0.0005915008,0.9933384,0.00007564974,0.0008558269],"genre_scores_gemma":[0.0001326505,0.001078099,0.000133082,0.001528011,0.001250429,0.00001645098,0.9949265,0.0000463843,0.00088841],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.008086445,"threshold_uncertainty_score":0.9998415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433727507085098,"score_gpt":0.491420124567033,"score_spread":0.4670828494961821,"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."}}