{"id":"W2727216584","doi":"","title":"Feature Relevance in Bayesian Network Classifiers and Application to Image Event Recognition.","year":2017,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Computer science; Relevance (law); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Bayesian network; Event (particle physics); Bayesian probability; Feature extraction; Machine learning","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"],"consensus_categories":[],"category_scores_codex":[0.003030394,0.0001196764,0.0001317935,0.00002939717,0.001316333,0.0007192026,0.001511484,0.0001087677,0.000002661733],"category_scores_gemma":[0.0002965949,0.00009222332,0.00006166373,0.0003821629,0.0002797723,0.0004935114,0.0006872314,0.0009047404,0.00005541179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001085826,"about_ca_system_score_gemma":0.0001236148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001285851,"about_ca_topic_score_gemma":0.00007518745,"domain_scores_codex":[0.9981097,0.0001914641,0.0001339996,0.0004938521,0.0004940366,0.0005769213],"domain_scores_gemma":[0.9980639,0.0002844798,0.00005831394,0.001177346,0.0002420638,0.0001739067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007568027,0.00008321419,0.001397404,0.0001005124,0.00005046581,0.00001696729,0.007210566,0.001568787,0.004034019,0.03580875,0.3727044,0.5769492],"study_design_scores_gemma":[0.0005219825,0.0001357703,0.01620082,0.0002362984,0.000005432219,0.00001138724,0.0003245484,0.7687631,0.000693045,0.1917969,0.02089965,0.0004110231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007189671,0.0001976987,0.9149696,0.07609115,0.0001786702,0.000490704,0.000004226438,0.00006012153,0.0008181201],"genre_scores_gemma":[0.9450259,0.0006548848,0.05133841,0.001417408,0.0005656021,0.0002569904,0.000004043115,0.00001646188,0.0007203554],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9378362,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05166454561908704,"score_gpt":0.3677323953062363,"score_spread":0.3160678496871493,"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."}}