{"id":"W4239059323","doi":"10.1515/iupac.88.0918","title":"Hyperplasia","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Metabolism and Genetic Disorders","field":"Biochemistry, Genetics and Molecular Biology","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":[0.001450233,0.001213096,0.001249342,0.00280802,0.0008390873,0.003051174,0.00208284,0.001502184,0.2391364],"category_scores_gemma":[0.01193353,0.0005232395,0.001545154,0.004620485,0.0003455115,0.002565818,0.002174484,0.00140083,0.217772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359099,"about_ca_system_score_gemma":0.002390474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01097031,"about_ca_topic_score_gemma":0.01991778,"domain_scores_codex":[0.997801,0.0003650718,0.000463186,0.0007427819,0.0004290618,0.0001988695],"domain_scores_gemma":[0.9958687,0.001107177,0.0004909766,0.001085142,0.001216855,0.0002311236],"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":[0.0001836991,0.00001821351,0.00169695,0.001489776,0.00004259077,0.00003585162,0.00003652263,0.0001053394,0.0001283243,0.001435503,0.9796959,0.01513133],"study_design_scores_gemma":[0.0001044723,0.00001251558,0.002811329,0.0006848457,0.00002714228,0.00008777375,0.00006571362,0.00008590737,0.0001555076,0.001527134,0.9944185,0.00001917595],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002101666,0.0002911676,0.0002425779,0.0001936436,0.00008457983,0.0000584915,0.992919,0.0004857368,0.005514601],"genre_scores_gemma":[0.0008383859,0.0003334172,0.0007364191,0.0004637869,0.00003268956,0.0002131485,0.9927595,0.0001617228,0.004460844],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2391364,"threshold_uncertainty_score":0.7999911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009962683247461522,"score_gpt":0.4002944314545981,"score_spread":0.3903317482071366,"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."}}