{"id":"W2186383633","doi":"","title":"AIRCRAFT PATH EXTRACTION FROM NOISY TARGET DATA","year":2008,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Heading (navigation); Computer science; Radar; Noise (video); Sensor fusion; Real-time computing; Ground truth; Radar tracker; Air traffic control; Path (computing); Representation (politics); Remote sensing; Algorithm; Data mining; Artificial intelligence; Geography","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":[0.0001715017,0.0001368051,0.0001408307,0.00004762031,0.0002529185,0.0001014014,0.001897336,0.000088555,0.0004530291],"category_scores_gemma":[0.00004691021,0.0001176652,0.00003380845,0.0002303023,0.00004025193,0.001365052,0.0007278891,0.0002085717,0.0006263141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001403621,"about_ca_system_score_gemma":0.00004672297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004652088,"about_ca_topic_score_gemma":0.00001773387,"domain_scores_codex":[0.9984208,0.0000569174,0.0002371388,0.000681841,0.0003384117,0.0002648667],"domain_scores_gemma":[0.9973209,0.000173623,0.00006977498,0.002268207,0.0000465828,0.0001209162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001330153,0.0002482607,0.007871171,0.000003470356,0.00002997968,0.0003235667,0.000400833,0.0007842432,0.001436418,0.005351118,0.9489228,0.0346148],"study_design_scores_gemma":[0.0004097917,0.00003767649,0.02029484,0.00001628792,0.00000629302,0.0001331714,0.0000260241,0.4613343,0.001325358,0.00228098,0.5137126,0.0004226867],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01075378,0.0002146327,0.9809136,0.0006453796,0.001422504,0.00006796312,0.00009091244,0.0006213986,0.00526977],"genre_scores_gemma":[0.4297219,0.0001772408,0.5672578,0.0007902278,0.000485909,0.000002818211,0.0005254498,0.00001236505,0.001026321],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.46055,"threshold_uncertainty_score":0.805021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05160767813751423,"score_gpt":0.2694315584739816,"score_spread":0.2178238803364674,"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."}}