{"id":"W2107743272","doi":"10.1109/icassp.2007.366232","title":"Incremental Learning of Stochastic Grammars with Graphical EM in Radar Electronic Support","year":2007,"lang":"en","type":"article","venue":"","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Department of National Defence; École de Technologie Supérieure","funders":"","keywords":"Computer science; Ambiguity; Context (archaeology); Rule-based machine translation; Radar; Stochastic context-free grammar; Artificial intelligence; Machine learning; Context-free grammar; Data mining; L-attributed grammar","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":[0.0006468119,0.0000864622,0.0001172866,0.0002541753,0.00003616501,0.00002260733,0.0002755427,0.00004330711,0.00002263031],"category_scores_gemma":[0.00002042249,0.00007576746,0.00002456348,0.0006957122,0.0000420137,0.0002425209,0.00004783833,0.0002014603,0.000006419305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009577996,"about_ca_system_score_gemma":0.00009658125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005113795,"about_ca_topic_score_gemma":0.0003319232,"domain_scores_codex":[0.9987852,0.00004159601,0.0002755154,0.0002439023,0.0003575249,0.0002962433],"domain_scores_gemma":[0.9995063,0.00009486126,0.0001065965,0.0001831519,0.00005511294,0.0000540015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001188453,0.0002509286,0.03781188,0.0000163202,0.0000266607,0.00001656874,0.0008990414,0.006253205,0.03593969,0.8854135,0.00001984665,0.03323356],"study_design_scores_gemma":[0.003031491,0.002096842,0.6345987,0.00006705237,0.00001661624,0.00008940225,0.0008986702,0.3209117,0.0252669,0.0121695,0.0001143329,0.0007388524],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3660761,0.000003842896,0.6335123,0.00007991271,0.00001404692,0.00008599526,6.628457e-8,0.00004421302,0.0001835179],"genre_scores_gemma":[0.9882371,5.568868e-7,0.01166199,0.00003221067,0.00000961864,0.000003879248,0.00000366235,0.000006565162,0.00004439277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8732439,"threshold_uncertainty_score":0.3089707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009346490318876284,"score_gpt":0.2334388213366585,"score_spread":0.2240923310177822,"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."}}