{"id":"W1965951245","doi":"10.1109/icassp.2002.5745639","title":"A multiple model approach for prediction using genetic algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Clutter; Series (stratigraphy); Computer science; Algorithm; Hidden Markov model; Time series; Genetic algorithm; Nonlinear system; Process (computing); Segmentation; Markov process; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning; Radar; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00008866015,0.0001017714,0.00009660171,0.00005662164,0.0001787235,0.0001082099,0.0003700536,0.00007154568,0.00001224199],"category_scores_gemma":[0.00001429066,0.00009083605,0.00005613975,0.00016172,0.00001777415,0.0002546867,0.0000959875,0.00006964286,0.000006594769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001976621,"about_ca_system_score_gemma":0.000007162685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001049711,"about_ca_topic_score_gemma":2.44184e-7,"domain_scores_codex":[0.9990249,0.00001695044,0.0001786259,0.0003698482,0.0001610437,0.0002485966],"domain_scores_gemma":[0.9993627,0.00004799055,0.00004017743,0.0004193458,0.00005575037,0.00007401119],"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":[0.000001385903,0.000102014,0.0001129506,0.000007481507,0.000007590387,8.205099e-7,0.0001581737,0.8580317,0.0001492333,0.0008020737,0.007873809,0.1327527],"study_design_scores_gemma":[0.0003276766,0.00002252155,0.00004559967,0.000003343839,0.000005197081,0.00001924372,0.000006710573,0.9982631,0.00009316191,0.0003655199,0.00073814,0.0001097681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004635591,0.0001034337,0.9976766,0.00002825054,0.0002290292,0.0002159889,0.00004037336,0.000309425,0.0009333697],"genre_scores_gemma":[0.03903672,0.00001551676,0.96017,0.0001306737,0.0001484394,0.00002177877,0.00001587603,0.00001048745,0.0004505619],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1402314,"threshold_uncertainty_score":0.3704187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05856148558248842,"score_gpt":0.2375705468170261,"score_spread":0.1790090612345376,"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."}}