{"id":"W4237015832","doi":"10.1515/iupac.73.0383","title":"Laurics","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan College","funders":"","keywords":"Table (database); Commission; Product (mathematics); Food science; Chemistry; Biochemistry; Organic chemistry; Polymer science; Computer science; Political science; Mathematics; Law; 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":[0.001586296,0.002531849,0.001857923,0.007022806,0.001568712,0.00469888,0.003079614,0.002411138,0.1692486],"category_scores_gemma":[0.02037937,0.0007221499,0.001689319,0.01125605,0.0005955552,0.003397455,0.003472459,0.001860714,0.3007307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001483535,"about_ca_system_score_gemma":0.003104274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01423776,"about_ca_topic_score_gemma":0.03136658,"domain_scores_codex":[0.9973621,0.0005687118,0.0004077193,0.0008476024,0.0004936765,0.0003203023],"domain_scores_gemma":[0.994561,0.001794004,0.000461658,0.001311071,0.00144428,0.0004279583],"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":[0.0000669158,0.00001199549,0.0006638261,0.0007396858,0.0000168151,0.00002382702,0.00002700154,0.0001336901,0.00004701998,0.0005549846,0.9932787,0.004435655],"study_design_scores_gemma":[0.00008299021,0.0000116218,0.00158478,0.0003658353,0.00001481147,0.00005827909,0.00006954616,0.0002100933,0.0001225302,0.001602966,0.9958571,0.00001949943],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001462327,0.0002424234,0.0001730197,0.0001934789,0.00008119656,0.00002410051,0.9959196,0.0006989688,0.002520976],"genre_scores_gemma":[0.0003858454,0.0001751624,0.0004207934,0.0001410472,0.00002549834,0.0001083405,0.9970988,0.0001722753,0.001472259],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1692486,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751214249066966,"score_gpt":0.4230052901815886,"score_spread":0.4054931476909189,"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."}}