{"id":"W3193959263","doi":"10.1051/itmconf/20171400002","title":"Explaining the Results of an Optimization-Based Decision Support System – A Machine Learning Approach","year":2017,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tetra Tech (Canada); Université Laval","funders":"","keywords":"Computer science; Machine learning; Classifier (UML); Artificial intelligence; Decision support system; Decision tree; Software; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005563844,0.0002512592,0.0005331424,0.000483672,0.00136059,0.003544901,0.00912536,0.00009546996,0.0001616959],"category_scores_gemma":[0.001932885,0.0001821858,0.000124019,0.000511788,0.0001314476,0.003853236,0.001359128,0.0005186548,0.000004960986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005802541,"about_ca_system_score_gemma":0.0002021489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001068829,"about_ca_topic_score_gemma":0.00002059564,"domain_scores_codex":[0.9964569,0.0007058625,0.001008209,0.0006149213,0.0009277397,0.0002863404],"domain_scores_gemma":[0.9939013,0.0006768435,0.002714475,0.002123952,0.0003956023,0.0001877942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005055157,0.0003410275,0.09949364,0.0001523525,0.0000863877,0.00002448157,0.0007976516,0.7742369,0.002077377,0.001541111,0.002334472,0.1184091],"study_design_scores_gemma":[0.0008679281,0.00002823962,0.08899961,0.0002840819,0.00002599987,0.00001667648,0.00009204654,0.9067988,0.0009189501,0.0001008323,0.001644106,0.0002227225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03065561,0.0007727484,0.953605,0.0003500181,0.0004709364,0.0004815343,0.00004820425,0.0001229721,0.01349295],"genre_scores_gemma":[0.9539235,0.000217892,0.04547023,0.00005051963,0.00008265479,0.00002485835,0.00009703579,0.00002727945,0.0001060608],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9232678,"threshold_uncertainty_score":0.9999395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2129425885132827,"score_gpt":0.5079612200032303,"score_spread":0.2950186314899476,"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."}}