{"id":"W2054976451","doi":"10.1109/csndsp.2014.6923836","title":"Discriminative kernel learning in ambiguity domain","year":2014,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Discriminative model; Artificial intelligence; Kernel (algebra); Pattern recognition (psychology); Dimensionality reduction; Machine learning; Signal processing; Frequency domain; Mathematics; Radar","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007657018,0.00006491071,0.00008532733,0.00009392873,0.00004294395,0.00008749116,0.000366699,0.00003598523,0.00001383601],"category_scores_gemma":[0.00008446714,0.00005633492,0.00002249108,0.0002143001,0.00002416829,0.0003460163,0.0001606372,0.0001626393,0.00004151841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002618303,"about_ca_system_score_gemma":0.00001644259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009271455,"about_ca_topic_score_gemma":0.0001036298,"domain_scores_codex":[0.9991758,0.0002299242,0.0001237969,0.0002047558,0.0001333182,0.0001323719],"domain_scores_gemma":[0.9996088,0.00008042221,0.00003934257,0.0002104672,0.00002773868,0.00003322537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[9.646897e-7,0.00003059719,0.00183853,0.000002012796,0.000001033495,0.00000109017,0.003472003,0.00007589855,0.0003443401,0.9669102,0.0003286784,0.02699468],"study_design_scores_gemma":[0.0008052986,0.0003404468,0.1182418,0.00003963096,0.000001765018,0.000007615102,0.0006395032,0.2448941,0.02662911,0.573204,0.03460207,0.0005945631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03940855,0.000002319681,0.8623449,0.001531581,0.00002414423,0.00005956837,3.857857e-8,0.0003630265,0.09626587],"genre_scores_gemma":[0.8940442,0.000001141592,0.1040862,0.0006196828,0.000009890085,0.000009977031,5.598069e-7,0.000003361117,0.001224968],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8546357,"threshold_uncertainty_score":0.2297271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273346822690638,"score_gpt":0.2660422310836952,"score_spread":0.2533087628567888,"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."}}