{"id":"W2948304566","doi":"10.48550/arxiv.1906.02397","title":"Obstructed Target Tracking in Urban Environments","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Particle filter; Tracking (education); Computer vision; A priori and a posteriori; Azimuth; Filter (signal processing); Artificial intelligence; Set (abstract data type); Trajectory; Geospatial analysis; Terrain; Real-time computing; Geography; Remote sensing; Cartography; Mathematics; Geometry","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":[0.000578361,0.0002462216,0.0003990348,0.0003389043,0.0002757851,0.0004623873,0.0004553027,0.0003566835,0.0003501725],"category_scores_gemma":[0.001826051,0.0002187423,0.0002602242,0.0007302055,0.0006118704,0.0005826253,0.0005890696,0.0003993417,0.0001177545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005138384,"about_ca_system_score_gemma":0.0005163195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038117,"about_ca_topic_score_gemma":0.008504811,"domain_scores_codex":[0.9997295,0.00006241307,0.00001125077,0.00006885043,0.00008848398,0.00003957965],"domain_scores_gemma":[0.9993795,0.0003324349,0.0001017011,0.00008459956,0.00007150168,0.00003021753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003551486,0.00001018053,0.002036832,0.00002085029,0.00001197478,0.00009510271,0.000056775,0.9765206,0.002003441,0.004930732,0.0001724035,0.01410556],"study_design_scores_gemma":[0.000002030758,0.00001020972,0.0009281944,0.000001618845,0.000002100373,0.00002353447,0.00001131159,0.9960672,0.0009393749,0.001755688,0.0002550989,0.000003557781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2085878,0.0001749753,0.7890224,0.00007687427,0.00003126313,0.00001583413,0.00009511345,0.0004209579,0.001574828],"genre_scores_gemma":[0.9236594,0.0002311053,0.07479226,0.000020548,0.00001257272,0.00001555751,0.0001532884,0.00002966351,0.0010857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01038117,"threshold_uncertainty_score":0.02064145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04700619948611347,"score_gpt":0.1802381146757172,"score_spread":0.1332319151896038,"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."}}