{"id":"W4251811261","doi":"10.1515/iupac.87.0252","title":"Flaccid","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; Chemical nomenclature; Relation (database); Computer science; Psychology; Chemistry; Linguistics; Philosophy; Data mining; Organic chemistry","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":[0.001620121,0.001952524,0.001631868,0.005410234,0.001081216,0.004545218,0.002994783,0.002301055,0.2240022],"category_scores_gemma":[0.0149366,0.0008268188,0.001875152,0.008579833,0.0004722191,0.003197636,0.003237009,0.001954973,0.2770778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001901537,"about_ca_system_score_gemma":0.003815407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02069647,"about_ca_topic_score_gemma":0.04014219,"domain_scores_codex":[0.9980697,0.0003396018,0.0003912119,0.0005632828,0.0003824058,0.0002538518],"domain_scores_gemma":[0.9948074,0.001553678,0.0006007379,0.001140349,0.001488388,0.0004094033],"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":[0.00006257483,0.000008139901,0.0004976564,0.001000805,0.00002179682,0.00001151751,0.00001683037,0.00008286095,0.00004224103,0.000419647,0.9950943,0.002741749],"study_design_scores_gemma":[0.0001756801,0.00001620198,0.001876293,0.0007043136,0.00002517232,0.00004315911,0.00005077128,0.0001478616,0.0001191768,0.001140821,0.9956771,0.00002349901],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003915148,0.00007041072,0.00004473969,0.00009130169,0.00002430185,0.00001336926,0.9983162,0.0003475354,0.001053081],"genre_scores_gemma":[0.0001733023,0.00008962605,0.0002161864,0.0001138571,0.00001240031,0.00008393614,0.9981382,0.0001295639,0.001042914],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2240022,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01559216811471067,"score_gpt":0.4237530101758674,"score_spread":0.4081608420611567,"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."}}