{"id":"W4243388782","doi":"10.1515/iupac.78.0494","title":"Precision","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Relation (database); Chemical nomenclature; Computer science; Data science; Management science; Chemistry; Engineering; Data mining; Linguistics","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":[0.001555644,0.0008484105,0.001059682,0.0006147786,0.0001523535,0.000119151,0.001195509,0.0007937744,0.02058882],"category_scores_gemma":[0.002527778,0.0006002475,0.0003305767,0.0004311893,0.0002477098,0.0002055051,0.0005354513,0.0008199037,0.0004422946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001533506,"about_ca_system_score_gemma":0.001504052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000103138,"about_ca_topic_score_gemma":0.00114697,"domain_scores_codex":[0.9937319,0.0002467409,0.0008245519,0.001052577,0.003310028,0.0008342216],"domain_scores_gemma":[0.9952348,0.0002562312,0.0005973972,0.002443533,0.001095774,0.0003722869],"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":[0.0005701791,0.0003100323,0.00000248433,0.00008486636,0.0001265444,0.0001233114,0.000004469407,7.668697e-7,0.00004846208,0.000003879866,0.9899389,0.00878612],"study_design_scores_gemma":[0.001595253,0.00027202,0.00001888534,0.001062775,0.0001949192,0.00003261529,0.000005452182,0.000002081157,0.00004265179,0.0003030133,0.9956763,0.0007940436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003953833,0.0009548988,0.00007730079,0.0002270209,0.00144526,0.0005787383,0.9962363,0.0003107228,0.0001302111],"genre_scores_gemma":[0.000002134914,0.000669332,0.0001059892,0.0001389756,0.002134679,0.00002489933,0.9955235,0.0002430674,0.001157445],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02014652,"threshold_uncertainty_score":0.9996449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725852627004463,"score_gpt":0.4298294800574559,"score_spread":0.4125709537874113,"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."}}