{"id":"W4248589802","doi":"10.1515/iupac.88.1120","title":"Nipple","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Cancer and Skin Lesions","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":[0.0002781271,0.0003380467,0.0008077917,0.0001725824,0.0002257684,0.00006206227,0.0003289569,0.0003856595,0.006918805],"category_scores_gemma":[0.0006980816,0.0002796719,0.0002745,0.00007373213,0.0001713572,0.0000442329,0.0001567866,0.0007582103,0.00001133664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003792602,"about_ca_system_score_gemma":0.001860583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005231155,"about_ca_topic_score_gemma":0.001779389,"domain_scores_codex":[0.9978764,0.00002260767,0.0003256623,0.0004335858,0.000988556,0.0003532001],"domain_scores_gemma":[0.9973811,0.00004018549,0.0002026794,0.001704443,0.0003849648,0.0002866386],"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":[0.0002704077,0.0002289339,0.00005009071,0.0002468065,0.0001177154,0.0005885237,0.000007496067,2.232779e-7,0.000004733283,0.000002033855,0.9962764,0.002206633],"study_design_scores_gemma":[0.001547973,0.0003010927,0.0003462006,0.0006463316,0.0004519917,0.0003029635,0.00001147044,0.000002748981,0.000008852814,0.00004669528,0.9960605,0.0002731836],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009352325,0.001351056,0.00002188343,0.001421922,0.001066621,0.0003517391,0.9951761,0.00005633546,0.0004607931],"genre_scores_gemma":[0.00004654541,0.001528334,0.00006581736,0.0006669033,0.002314729,0.00001605849,0.9921961,0.00003934727,0.003126206],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.006907468,"threshold_uncertainty_score":0.9999655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02614951733892364,"score_gpt":0.4846767275109651,"score_spread":0.4585272101720415,"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."}}