{"id":"W1994406310","doi":"10.1109/camsap.2013.6714002","title":"Syntactic track-before-detect","year":2013,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Track-before-detect; Track (disk drive); Artificial intelligence; Stochastic context-free grammar; Rule-based machine translation; Hidden Markov model; Trajectory; Markov chain; Generalization; Context-free grammar; Particle filter; Context (archaeology); Range (aeronautics); Markov process; Algorithm; Machine learning; Tree-adjoining grammar; Mathematics; Kalman filter","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001618242,0.001003536,0.00140401,0.001094405,0.001123792,0.001660882,0.002027752,0.001689333,0.004682671],"category_scores_gemma":[0.004601629,0.0007296149,0.001440599,0.0009558407,0.001203155,0.002364432,0.002447749,0.001707158,0.002811511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007906777,"about_ca_system_score_gemma":0.003104537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003179129,"about_ca_topic_score_gemma":0.006120335,"domain_scores_codex":[0.9986386,0.0002033949,0.00006083278,0.00035469,0.000632362,0.0001101364],"domain_scores_gemma":[0.9975381,0.0009236361,0.0002223704,0.0007040977,0.0005286197,0.00008320811],"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":[0.0004622196,0.0002715943,0.004576621,0.0003673417,0.0001830004,0.0007272305,0.0003671456,0.08311393,0.04391623,0.1184508,0.02080373,0.7267601],"study_design_scores_gemma":[0.00005374286,0.0001802707,0.001120166,0.00003035137,0.00009482367,0.0006075383,0.00007264104,0.8677611,0.04389195,0.06055295,0.02554275,0.00009171844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004639205,0.0000581586,0.9912983,0.0001174636,0.00007399739,0.00006584389,0.000127193,0.002124084,0.001495841],"genre_scores_gemma":[0.1697697,0.0002507129,0.8197783,0.0006047314,0.0001216821,0.000211056,0.001359467,0.0009948196,0.006909627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004682671,"threshold_uncertainty_score":0.01566505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01018277707406494,"score_gpt":0.2186233774727336,"score_spread":0.2084406003986687,"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."}}