{"id":"W4248043122","doi":"10.1515/iupac.88.0507","title":"Artificial Insemination","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); Human reproduction; Computer science; Biology; Linguistics; Genetics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001923615,0.001131307,0.001258476,0.002004666,0.0006932506,0.001910176,0.002115118,0.001385617,0.08420583],"category_scores_gemma":[0.01198232,0.0005089568,0.001903562,0.003142818,0.0003914019,0.001217344,0.001533505,0.001652142,0.05094388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128731,"about_ca_system_score_gemma":0.002437296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008956212,"about_ca_topic_score_gemma":0.01700093,"domain_scores_codex":[0.9979388,0.0004735432,0.0005327923,0.0005601154,0.0003572355,0.0001374494],"domain_scores_gemma":[0.9943217,0.002348949,0.0009621357,0.001318688,0.0008207402,0.0002277968],"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":[0.000655803,0.0000595347,0.004405262,0.005433051,0.000213067,0.00008377655,0.00004522187,0.0004749439,0.0002366611,0.001203795,0.9530366,0.03415233],"study_design_scores_gemma":[0.000439601,0.00005963278,0.008784758,0.002226705,0.0001429463,0.0002094958,0.00006537242,0.0002665154,0.0004077359,0.002044553,0.9853112,0.00004157429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004292313,0.0006845935,0.0004060816,0.0001407566,0.00009716451,0.0001139961,0.9949044,0.0002546481,0.002969132],"genre_scores_gemma":[0.001751778,0.0009337367,0.001427567,0.0004829425,0.00003944898,0.0007408925,0.9915639,0.0001163193,0.002943378],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08420583,"threshold_uncertainty_score":0.2816966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488130252194258,"score_gpt":0.4650241914420808,"score_spread":0.4501428889201382,"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."}}