{"id":"W4245241272","doi":"10.1515/iupac.88.0519","title":"Atrophy","year":2017,"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; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001829918,0.001547158,0.001365849,0.004191174,0.001169269,0.004404857,0.002604425,0.001833679,0.3154542],"category_scores_gemma":[0.01713699,0.00069733,0.001884989,0.007850617,0.0004336106,0.003855373,0.002907802,0.00181873,0.3305736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002050264,"about_ca_system_score_gemma":0.00345899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01927261,"about_ca_topic_score_gemma":0.03095976,"domain_scores_codex":[0.9968225,0.0005177686,0.0006788062,0.0009737666,0.0006878533,0.0003193337],"domain_scores_gemma":[0.9921101,0.001891455,0.0007454987,0.001869754,0.002992651,0.0003906125],"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":[0.00006751545,0.000009510627,0.0007648697,0.0007752068,0.00001948659,0.00001176831,0.00002488005,0.00005867427,0.00004873782,0.0008948947,0.9903992,0.006925259],"study_design_scores_gemma":[0.00007777339,0.0000100004,0.002328148,0.0005910893,0.00001695659,0.00004276539,0.00007643914,0.00008052522,0.000103638,0.001286141,0.9953681,0.0000183931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001038554,0.0001249994,0.0001318113,0.0001766529,0.0000725269,0.000037347,0.9947839,0.0003718659,0.004197027],"genre_scores_gemma":[0.0004318464,0.0001739509,0.0004504651,0.0002794085,0.0000265191,0.0001787178,0.9946356,0.0001744747,0.003649015],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6845458,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129656547825262,"score_gpt":0.4582090101784115,"score_spread":0.4369124447001589,"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."}}