{"id":"W3006608869","doi":"10.1109/cac48633.2019.8996426","title":"Track Matching Based on ELM for HFSWR","year":2019,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Track (disk drive); Matching (statistics); Interference (communication); Radar; Key (lock); Tracking (education); Radar tracker; Artificial intelligence; Field (mathematics); Telecommunications","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":[0.001236499,0.0004142849,0.0007166912,0.0008764247,0.0004644025,0.0006028618,0.001094194,0.0008621546,0.001269112],"category_scores_gemma":[0.002860085,0.0002462533,0.0007490176,0.001034344,0.000459852,0.001294647,0.001296359,0.0008422033,0.0004761821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005813642,"about_ca_system_score_gemma":0.0005870708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00162875,"about_ca_topic_score_gemma":0.001222882,"domain_scores_codex":[0.9991711,0.0001704149,0.00005295818,0.000211822,0.0003106097,0.000082956],"domain_scores_gemma":[0.9993392,0.0002230096,0.0001088971,0.0001254419,0.0001743563,0.00002908758],"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":[0.0001598574,0.0001340713,0.003879937,0.00006634704,0.00008306615,0.0001463328,0.0001222401,0.4821537,0.01399309,0.009868651,0.00155814,0.4878345],"study_design_scores_gemma":[0.000004134621,0.00001654217,0.0003563756,0.000001584224,0.000004005376,0.00003092632,0.00000678962,0.9955359,0.001546068,0.002196142,0.000296205,0.00000539408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01355836,0.00004538711,0.9855372,0.00003991823,0.0000152154,0.00001347943,0.00001522604,0.0002821104,0.0004930926],"genre_scores_gemma":[0.621974,0.000112548,0.3744026,0.0001680694,0.00004781499,0.0001094799,0.0002567033,0.0000986599,0.002830111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00162875,"threshold_uncertainty_score":0.006539285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008481095032104102,"score_gpt":0.2487777247220232,"score_spread":0.2402966296899191,"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."}}