{"id":"W4250784575","doi":"10.1515/iupac.87.0081","title":"Atrophy","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Relation (database); Chemical nomenclature; Psychology; Computer science; Medicine; Chemistry; Linguistics; Philosophy; Organic chemistry; 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":[0.001371878,0.001605592,0.001231692,0.003196561,0.001126795,0.003742571,0.002474617,0.001714931,0.2471271],"category_scores_gemma":[0.01284811,0.0005960938,0.001765853,0.005420289,0.0003920453,0.002859736,0.002428646,0.001677239,0.2825727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001831533,"about_ca_system_score_gemma":0.002827443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01900372,"about_ca_topic_score_gemma":0.03271386,"domain_scores_codex":[0.9975533,0.0003675325,0.0004592124,0.0008540005,0.0004877361,0.0002782436],"domain_scores_gemma":[0.9947113,0.001084212,0.0005002936,0.001363824,0.002036683,0.0003037282],"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":[0.0001061938,0.00001447105,0.001500113,0.0006083344,0.00002499605,0.00001683031,0.00002409435,0.00008597716,0.0000600041,0.0007859268,0.9887838,0.007989256],"study_design_scores_gemma":[0.0001157748,0.00001659025,0.003727465,0.0004395931,0.00002460777,0.00007745923,0.00009160963,0.0001523809,0.0001649223,0.001504093,0.9936644,0.00002113336],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002011306,0.0001380198,0.0001415294,0.0001912244,0.00008035358,0.00003740946,0.9945043,0.0004328994,0.004273165],"genre_scores_gemma":[0.0006882425,0.0001435984,0.0004507109,0.0002569566,0.0000263298,0.0001448868,0.9942602,0.0001238283,0.003905224],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2471271,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431463232190026,"score_gpt":0.4140544815711908,"score_spread":0.3997398492492905,"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."}}