{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002376802,0.0003900628,0.0004302485,0.0003405011,0.0001015962,0.0001636582,0.002488344,0.0004746053,0.0000837742],"category_scores_gemma":[0.00002942174,0.0004712139,0.0001730621,0.0005423169,0.00008059742,0.0005140902,0.002322686,0.001098571,0.0002619404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002487749,"about_ca_system_score_gemma":0.00008874635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009995099,"about_ca_topic_score_gemma":0.00001340933,"domain_scores_codex":[0.9972656,0.000196506,0.0003183332,0.001521922,0.0001549671,0.0005426906],"domain_scores_gemma":[0.9975245,0.0001384182,0.0002824805,0.001867657,0.00003077987,0.0001561712],"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":[0.00002612931,0.0001435087,0.07538987,0.00005113314,0.00005016382,0.0006183803,0.00040438,0.8904547,0.00007363741,0.03034797,0.001400931,0.001039169],"study_design_scores_gemma":[0.0009705576,0.00003718796,0.03914839,0.000223209,0.00002716316,0.000007282411,0.00005693772,0.9321588,0.0001787899,0.01698406,0.009269429,0.0009381359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3750637,0.0001768628,0.6193557,0.00004904274,0.002296324,0.0003887936,0.00004686484,0.0002766839,0.002345992],"genre_scores_gemma":[0.9935373,0.0001748197,0.004665027,0.00009393302,0.00009788485,6.870249e-7,0.00009077047,0.00002734425,0.001312256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6184735,"threshold_uncertainty_score":0.999774,"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."}}