{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001924107,0.00006249393,0.00006877982,0.00004361344,0.00004903936,0.00007149085,0.0003317834,0.00002457252,0.00007674273],"category_scores_gemma":[0.000006894443,0.00004841116,0.00004811018,0.00007462923,0.000002651539,0.00008597139,0.00002646875,0.00005672006,0.0004594747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008017556,"about_ca_system_score_gemma":0.00001247879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001581189,"about_ca_topic_score_gemma":0.000001298997,"domain_scores_codex":[0.9994381,0.00001767342,0.00006964667,0.0002089538,0.0001109777,0.0001546275],"domain_scores_gemma":[0.9994874,0.0001225619,0.00002052385,0.0003202109,0.00001222362,0.00003710609],"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":[0.00004843799,0.0002214444,0.002431195,0.0001380368,0.00001707295,0.00000559902,0.0006138779,0.04190858,0.0006615361,0.5453989,0.01324247,0.3953129],"study_design_scores_gemma":[0.0005671197,0.0001581022,0.002149084,0.00001820315,0.000001347803,0.000001436115,0.000009349073,0.9567767,0.0004565923,0.003371605,0.03636415,0.0001263421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04542315,0.000002899969,0.8335716,0.00202259,0.0002856782,0.0001448916,4.16139e-7,0.0002540258,0.1182948],"genre_scores_gemma":[0.8644141,1.679858e-7,0.1221923,0.001945554,0.00003273005,0.000006551477,0.000001247148,0.000006016788,0.01140138],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9148681,"threshold_uncertainty_score":0.5905771,"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."}}