{"id":"W2151840714","doi":"10.1109/icccn.1999.805583","title":"A pragmatic approach for feature interaction detection in intelligent networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Feature (linguistics); Point (geometry); Artificial intelligence; Feature detection (computer vision); Object (grammar); Feature extraction; Object detection; Interaction information; Data mining; Pattern recognition (psychology); Image (mathematics); Image processing; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009459865,0.00177644,0.001777384,0.004453728,0.002946037,0.007270727,0.004351539,0.003788852,0.004849711],"category_scores_gemma":[0.0341153,0.001525001,0.002694701,0.001772356,0.006359387,0.01074886,0.005446422,0.004509508,0.0009512745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003984031,"about_ca_system_score_gemma":0.003345049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004648948,"about_ca_topic_score_gemma":0.004289599,"domain_scores_codex":[0.9768868,0.01019564,0.00165397,0.002966546,0.007391924,0.0009051658],"domain_scores_gemma":[0.9785001,0.01240997,0.001558142,0.003242344,0.003713754,0.0005756211],"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":[0.0001976644,0.0002214852,0.002904613,0.0005003875,0.0001454824,0.0006764131,0.001991917,0.02895113,0.008623132,0.7622222,0.006916628,0.186649],"study_design_scores_gemma":[0.00005561056,0.000101992,0.0009985334,0.00008258133,0.00008450083,0.0006201284,0.0005001629,0.4347741,0.008897116,0.5254489,0.02826582,0.0001705839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002301573,0.00005323564,0.9943547,0.0005172819,0.00002856338,0.0001208551,0.00005587374,0.0005176967,0.002050289],"genre_scores_gemma":[0.1075363,0.00006452422,0.8894439,0.0002778649,0.00007793758,0.0003411504,0.0002256313,0.0001954759,0.001837249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009459865,"threshold_uncertainty_score":0.05002916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829100596627505,"score_gpt":0.2582038772952125,"score_spread":0.2399128713289375,"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."}}