{"id":"W94369806","doi":"10.3233/ica-2007-14204","title":"A genetic feature weighting scheme for pattern recognition","year":2007,"lang":"en","type":"article","venue":"Integrated Computer-Aided Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Concordia University","keywords":"Weighting; Pattern recognition (psychology); Scheme (mathematics); Feature (linguistics); Artificial intelligence; Computer science; Mathematics; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001868216,0.0002221729,0.0001745535,0.0001492962,0.0001053814,0.0001861205,0.0005252079,0.0001089587,0.000002963621],"category_scores_gemma":[0.00001361496,0.0002106441,0.00009978448,0.000554829,0.000009188711,0.0002066647,0.00009256211,0.0002760073,0.00001974423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006035258,"about_ca_system_score_gemma":0.00001832344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001480255,"about_ca_topic_score_gemma":0.000006930401,"domain_scores_codex":[0.9987109,0.000009142918,0.0002821507,0.0004138638,0.0001295651,0.0004543315],"domain_scores_gemma":[0.9991227,0.0001899773,0.00007746014,0.0003363289,0.000147515,0.0001260423],"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":[0.000004419573,0.00004182159,0.00009818385,0.00004125412,0.00004009987,0.00002554715,0.0001000117,0.009997346,0.01275849,0.00122644,0.002544294,0.9731221],"study_design_scores_gemma":[0.0002637495,0.0000640219,0.0009405326,0.0001170713,0.000005763063,0.00005262886,0.000004293679,0.9833569,0.005864871,0.0003561481,0.008694533,0.0002794797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06102458,0.00008803861,0.9371269,0.0003170576,0.0005134583,0.0002957478,0.00000561935,0.0005790614,0.00004953751],"genre_scores_gemma":[0.3116014,0.000006928717,0.6874498,0.0002479168,0.0005631066,0.00005437927,0.00002379377,0.00002672142,0.00002596025],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9733596,"threshold_uncertainty_score":0.8589818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226931836003458,"score_gpt":0.2165386795217848,"score_spread":0.2042693611617502,"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."}}