{"id":"W2781697945","doi":"10.2514/6.2018-0465","title":"Important Considerations for Cooperative Passive Ranging of Aircraft","year":2018,"lang":"en","type":"article","venue":"2018 AIAA Information Systems-AIAA Infotech @ Aerospace","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Ranging; Computer science; Aeronautics; Telecommunications; Engineering","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":[0.001938672,0.0008161666,0.0006249451,0.0005170866,0.001347678,0.002046405,0.001342984,0.003081122,0.006478717],"category_scores_gemma":[0.01288295,0.0004851089,0.00030687,0.0004884707,0.00175519,0.005415551,0.002101765,0.002477753,0.001974548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000631601,"about_ca_system_score_gemma":0.0007111907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006875792,"about_ca_topic_score_gemma":0.001003862,"domain_scores_codex":[0.9987633,0.0003247837,0.00005258195,0.0001927832,0.0005735204,0.00009302324],"domain_scores_gemma":[0.9944347,0.003902327,0.0002075079,0.0004790053,0.000839675,0.0001369732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003320844,0.00008304558,0.001253117,0.001058919,0.00005262914,0.001210682,0.0006416711,0.08132083,0.05320398,0.6591148,0.0156188,0.1861096],"study_design_scores_gemma":[0.00006607756,0.0003829197,0.001521727,0.0003043919,0.00007163655,0.002969255,0.0006686804,0.1296331,0.02008618,0.764424,0.07975493,0.0001170869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01635772,0.01563654,0.870749,0.0236753,0.002564585,0.00008815667,0.0001109786,0.000340374,0.07047734],"genre_scores_gemma":[0.786669,0.01204949,0.1590321,0.003531198,0.002857503,0.0002594284,0.0002324605,0.0002865471,0.03508212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006478717,"threshold_uncertainty_score":0.0216735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880750769768184,"score_gpt":0.2499397485889593,"score_spread":0.2311322408912775,"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."}}