{"id":"W2117330767","doi":"10.1109/ccece.2006.277823","title":"SVM Classifier Approach to Enumerate Directional Signals Impinging on an Array of Sensors","year":2006,"lang":"en","type":"article","venue":"","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Support vector machine; Pattern recognition (psychology); Computer science; Classifier (UML); Generative model; A priori and a posteriori; Sensor array; Artificial intelligence; Signal processing; Enumeration; Statistical model; Machine learning; Algorithm; Generative grammar; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003555351,0.0001176622,0.0001758647,0.000300889,0.0000623525,0.0000549031,0.0003148543,0.00005014889,0.00002273839],"category_scores_gemma":[0.00003570254,0.0001076608,0.0000605349,0.0005727791,0.00003138984,0.0003763539,0.00003791803,0.00006954405,0.0000119287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004006919,"about_ca_system_score_gemma":0.00003817608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002028047,"about_ca_topic_score_gemma":0.000003592424,"domain_scores_codex":[0.9987854,0.000076857,0.000325601,0.0003175256,0.0003363789,0.0001582055],"domain_scores_gemma":[0.999215,0.00008151219,0.0001314408,0.0003363317,0.0001734166,0.00006231318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003072555,0.0009673845,0.001289819,0.00005697337,0.0000388384,0.000001292391,0.0006674348,0.06893951,0.476091,0.4165047,0.004261503,0.03115078],"study_design_scores_gemma":[0.00007974976,0.0001300971,0.003847917,0.00002654182,0.000002939617,0.000005191492,0.00001554709,0.06246648,0.9282839,0.004406563,0.0005721102,0.0001629822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03716833,0.000002857048,0.8576515,0.0001330902,0.0001113013,0.0001534855,0.000002377343,0.0003559668,0.1044211],"genre_scores_gemma":[0.6931055,3.470252e-7,0.3061073,0.00007528298,0.00003568454,0.00001601127,0.000002455849,0.000007196278,0.0006502786],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6559371,"threshold_uncertainty_score":0.439028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180116494730534,"score_gpt":0.2695253246087513,"score_spread":0.247724159661446,"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."}}