{"id":"W2044195225","doi":"10.1109/icassp.2007.366799","title":"Threat Estimation of Multifunction Radars: Modeling and Statistical Signal Processing of Stochastic Context Free Grammars","year":2007,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Estimator; Context (archaeology); SIGNAL (programming language); Synchronous context-free grammar; Hidden Markov model; Statistical signal processing; Stochastic context-free grammar; Artificial intelligence; Markov chain; Rule-based machine translation; Bayesian probability; Signal processing; Markov process; Context-free grammar; Machine learning; Radar; Mathematics; Statistics; Context-sensitive 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":[0.0004240377,0.00009366813,0.0001645227,0.00008994107,0.00006902678,0.00003877381,0.0001949829,0.00005931379,0.00001104956],"category_scores_gemma":[0.00008612082,0.00008027766,0.00001896086,0.0001552143,0.00007736339,0.0003197678,0.00009920136,0.00009131205,7.796612e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001219101,"about_ca_system_score_gemma":0.0000278424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001664961,"about_ca_topic_score_gemma":0.00002851141,"domain_scores_codex":[0.9989352,0.00002162341,0.0003786071,0.000227789,0.000273646,0.0001630822],"domain_scores_gemma":[0.9991689,0.0002946442,0.0001160743,0.0002176995,0.0001383427,0.00006434019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005330744,0.00006342465,0.00009071644,0.00005154132,0.000006845787,0.000002090606,0.000387614,0.219548,0.000412056,0.0299455,0.00006904639,0.7493699],"study_design_scores_gemma":[0.0003895379,0.00008421191,0.000154461,0.0000763041,0.000009979268,0.000009847154,0.000119365,0.9896648,0.0004828689,0.008924631,0.000002027766,0.00008196869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02823321,0.0001274383,0.9713092,0.00002355824,0.000080378,0.00009373905,0.000007899323,0.00006087396,0.00006365523],"genre_scores_gemma":[0.6528927,0.000001467314,0.3470641,0.00001224748,0.00001326312,6.14189e-7,0.000007124244,0.000003708675,0.000004750946],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7701167,"threshold_uncertainty_score":0.3273628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01963173789910957,"score_gpt":0.25746778790103,"score_spread":0.2378360500019204,"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."}}