{"id":"W2099608078","doi":"10.1109/iscas.2005.1465696","title":"A Spatial Variance Approach to Target Tracking with Sensor Arrays","year":2005,"lang":"en","type":"article","venue":"","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Tracking (education); Computer science; Tracking system; Computer vision; Artificial intelligence; Task (project management); Variance (accounting); Real-time computing; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004680703,0.0001354748,0.0001245603,0.00004537991,0.00003546232,0.00004777521,0.00008353066,0.00002984424,0.00008382896],"category_scores_gemma":[0.000006152086,0.0001129354,0.00002393999,0.0001375104,0.00001172461,0.0001038702,0.000007667805,0.0001091673,0.0001250863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003458405,"about_ca_system_score_gemma":0.000006830819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003049391,"about_ca_topic_score_gemma":0.00002133511,"domain_scores_codex":[0.9993303,0.000006426411,0.0001071605,0.0001711746,0.0001237779,0.0002611307],"domain_scores_gemma":[0.9996976,0.00001108062,0.000008287627,0.0001731801,0.00002022711,0.0000896404],"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":[0.00001968901,0.00006888019,0.0004655487,0.00004051376,0.00004695165,0.00001451849,0.001656532,0.9600513,0.009839626,0.0008268584,0.003131417,0.02383819],"study_design_scores_gemma":[0.0007186626,0.0000443289,0.002504422,0.00003696572,0.0000208293,0.0001746905,0.000337092,0.8368592,0.03270182,0.00003559297,0.125839,0.0007274497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04774588,0.00002870942,0.8033116,0.0002666608,0.0000771298,0.000135998,0.000003344636,0.0006122151,0.1478185],"genre_scores_gemma":[0.7778495,0.000001129302,0.2205587,0.0002172934,0.0002792144,0.000007990394,0.000002527352,0.00003263306,0.001051049],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7301036,"threshold_uncertainty_score":0.4605372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008257176760329065,"score_gpt":0.1883005077740453,"score_spread":0.1800433310137163,"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."}}