{"id":"W2107505687","doi":"10.1016/j.asoc.2012.02.022","title":"Graphical EM for on-line learning of grammatical probabilities in radar Electronic Support","year":2012,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Department of National Defence; École de Technologie Supérieure","funders":"","keywords":"Computer science; Perplexity; Radar; Artificial intelligence; Machine learning; Context (archaeology); Ambiguity; Line (geometry); Online machine learning; Active learning (machine learning); Language model","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":[0.001151772,0.0001380735,0.0002460277,0.0001582254,0.00009992964,0.00003555101,0.0003437265,0.00008418111,0.000003003809],"category_scores_gemma":[0.0001469238,0.0001392727,0.00006417196,0.0003886457,0.00005307817,0.0001339474,0.00009640712,0.0002784479,0.000007939315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008641848,"about_ca_system_score_gemma":0.0000754253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002251274,"about_ca_topic_score_gemma":0.000001843988,"domain_scores_codex":[0.9983365,0.00006557052,0.0004887914,0.0002960968,0.0002818469,0.0005311761],"domain_scores_gemma":[0.9985276,0.0008928242,0.000190603,0.0002536877,0.00006411968,0.00007119332],"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":[0.000018545,0.0001464953,0.002129757,0.00005855402,0.00000829167,1.149312e-7,0.001492089,0.01128868,0.001874447,0.950324,0.000009263273,0.03264977],"study_design_scores_gemma":[0.0008841788,0.0002976583,0.01057313,0.00004293847,0.000007917212,0.000004476994,0.0002689334,0.9163661,0.00513486,0.06583112,0.0002755513,0.0003131316],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2573448,0.00001035033,0.7417885,0.0001605677,0.00005976239,0.0003470532,2.75675e-7,0.0001053651,0.0001832595],"genre_scores_gemma":[0.9509383,4.801282e-7,0.04884528,0.00006137542,0.00008714219,0.00003438606,0.000009434405,0.00001400069,0.000009596596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9050774,"threshold_uncertainty_score":0.5679374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02738446883685724,"score_gpt":0.2707583528931735,"score_spread":0.2433738840563162,"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."}}