{"id":"W1979980829","doi":"10.1002/int.20476","title":"Alternative approach for learning and improving the MCDA method PROAFTN","year":2011,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Preprocessor; Data pre-processing; Multiple-criteria decision analysis; Data mining; Construct (python library); Measure (data warehouse); Mathematics; Mathematical optimization","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":[0.00165955,0.0001177928,0.0001751686,0.0001377405,0.00008475004,0.0002627506,0.001129547,0.00004378182,0.00000278876],"category_scores_gemma":[0.0001768047,0.00007441062,0.0001015271,0.00006823889,0.00003571717,0.0003447012,0.000141278,0.0002605941,0.000001907913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005799219,"about_ca_system_score_gemma":0.00006553434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001083204,"about_ca_topic_score_gemma":5.867509e-7,"domain_scores_codex":[0.9985862,0.0001598138,0.0005125483,0.0001883729,0.0004054225,0.0001476257],"domain_scores_gemma":[0.9981658,0.0002162418,0.0005711706,0.0001126585,0.0008624274,0.0000716303],"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":[0.0002374868,0.0002963513,0.001780153,0.0001135243,0.001075865,0.00005036287,0.02013205,0.01907557,0.002911135,0.5843987,0.0003228227,0.3696059],"study_design_scores_gemma":[0.0002390773,0.0002546233,0.00003689102,0.00009965464,0.00001900029,0.0006633092,0.0009505843,0.983683,0.006074185,0.006741145,0.001101571,0.0001369736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001384213,0.0005942166,0.995446,0.0001184761,0.001346321,0.0001775565,0.000001500213,0.00001772308,0.0009139822],"genre_scores_gemma":[0.8329241,0.00006235044,0.1664032,0.00005898409,0.0003485146,0.00001810547,6.156462e-7,0.000008450871,0.0001757181],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9646074,"threshold_uncertainty_score":0.3034377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07620748980444986,"score_gpt":0.3218283083797058,"score_spread":0.245620818575256,"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."}}