{"id":"W4232710374","doi":"10.1515/iupac.79.1147","title":"Diploid","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Chemistry; Biology; Philosophy; Linguistics","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":[0.001493701,0.001826936,0.001419716,0.003649269,0.001043738,0.003521484,0.002622124,0.00182192,0.185417],"category_scores_gemma":[0.0122139,0.0005907141,0.001858123,0.006656866,0.0003720086,0.002578285,0.002219834,0.001794157,0.2410259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736468,"about_ca_system_score_gemma":0.003061121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01908058,"about_ca_topic_score_gemma":0.03356052,"domain_scores_codex":[0.9974918,0.0004233455,0.0004171945,0.0008930886,0.0004970606,0.0002775803],"domain_scores_gemma":[0.9952578,0.001123426,0.0004527049,0.001256525,0.001601437,0.0003079986],"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":[0.00008460068,0.00001491876,0.001080507,0.0006959391,0.0000321958,0.00001486604,0.0000195689,0.0001266695,0.0000714575,0.000647978,0.9913885,0.005822746],"study_design_scores_gemma":[0.0001259671,0.00001515998,0.002756569,0.0004775319,0.00003256302,0.00005438882,0.00007748115,0.0001814431,0.0001591596,0.001428368,0.9946691,0.00002224751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000899747,0.00008230669,0.000094264,0.000081558,0.00003633535,0.00002029705,0.9979935,0.0002083999,0.001393359],"genre_scores_gemma":[0.0003042321,0.0000808503,0.0003213702,0.0001268592,0.00001130364,0.0001071956,0.9976333,0.00006529741,0.001349564],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.185417,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623396766223917,"score_gpt":0.4234473761752329,"score_spread":0.4072134085129938,"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."}}