{"id":"W2079354075","doi":"10.1080/03610910701790475","title":"Statistical Discrimination Analysis Using the Maximum Function","year":2008,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Function (biology); Bayes' theorem; Relation (database); Computer science; Mathematics; Pattern recognition (psychology); Cluster (spacecraft); Algorithm; Statistics; Artificial intelligence; Data mining; Bayesian probability","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":[0.0005305737,0.0001576972,0.0002649748,0.0002639392,0.0006324311,0.0000488785,0.0001841623,0.00006907284,0.00002584699],"category_scores_gemma":[0.0009245115,0.0001368704,0.00003665114,0.0007273292,0.0003469588,0.0001804869,0.0001206204,0.0002312131,0.000002088513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008940324,"about_ca_system_score_gemma":0.00004188148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005191874,"about_ca_topic_score_gemma":0.0001298054,"domain_scores_codex":[0.9981453,0.0005426646,0.0006036112,0.0002558028,0.0002729207,0.0001796744],"domain_scores_gemma":[0.993372,0.005561137,0.0002290862,0.0005184285,0.0002563213,0.00006299877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003981495,0.0001529646,0.001905061,0.00002714331,0.00008408292,0.0000025051,0.001497082,0.3300601,0.00001570058,0.6219148,0.00005356582,0.04424712],"study_design_scores_gemma":[0.0002092468,0.0000216411,0.01509997,0.000006801338,0.0001905107,0.00000271231,0.0001997959,0.5801759,8.265874e-7,0.4039398,0.00005967107,0.00009313958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01450606,0.00008799144,0.9844706,0.0001004385,0.00005909659,0.0003311441,0.0001387456,0.00004644797,0.0002594911],"genre_scores_gemma":[0.5394359,0.00004270489,0.4602624,0.00004014244,0.00001024928,0.00001530687,0.0001668985,0.000010668,0.0000157727],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5249298,"threshold_uncertainty_score":0.5581412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4256811627931746,"score_gpt":0.5380505317313399,"score_spread":0.1123693689381653,"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."}}