{"id":"W2579972301","doi":"10.1093/jat/bkx001","title":"Procedure for the Selection and Validation of a Calibration Model I—Description and Application","year":2017,"lang":"en","type":"article","venue":"Journal of Analytical Toxicology","topic":"Forensic Toxicology and Drug Analysis","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Selection (genetic algorithm); Model selection; Model validation; Computer science; Statistics; Mathematics; Artificial intelligence; Data science","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001246891,0.0001118561,0.0003182533,0.0001235039,0.0005235458,0.00003588846,0.0001636367,0.0003446109,0.00002574109],"category_scores_gemma":[0.0007519439,0.00007976527,0.0001024219,0.00007132123,0.0005047316,0.0002835637,0.00004249364,0.0003736448,5.003458e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003445028,"about_ca_system_score_gemma":0.000100405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005112222,"about_ca_topic_score_gemma":0.00003971895,"domain_scores_codex":[0.9989631,0.0001344818,0.0004810068,0.0001573777,0.0001020235,0.0001620424],"domain_scores_gemma":[0.9982128,0.0004980176,0.0007734357,0.000125004,0.0002906271,0.0001001444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009457286,0.002081978,0.1606946,0.0006188976,0.004007563,0.00001166358,0.002259249,0.1194125,0.333568,0.2356162,0.01467519,0.1175969],"study_design_scores_gemma":[0.00128734,0.000603364,0.01106485,0.000008092755,0.001085154,0.00009711152,0.00009218044,0.959883,0.01354791,0.01100144,0.001239366,0.00009023762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8716879,0.0002650306,0.1191756,0.008009799,0.0002347174,0.0003695667,0.0000123054,0.000008144289,0.0002369899],"genre_scores_gemma":[0.9981012,0.0002347872,0.0009021068,0.0003337326,0.0002002111,0.00001784315,0.000003147871,0.000007691707,0.0001993234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8404704,"threshold_uncertainty_score":0.4026744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09206033682606128,"score_gpt":0.423701512005315,"score_spread":0.3316411751792537,"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."}}