{"id":"W1848596129","doi":"","title":"A track scoring MOP for perimeter surveillance radar evaluation","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Track (disk drive); Radar tracker; Measure (data warehouse); Track-before-detect; Consistency (knowledge bases); Radar; Real-time computing; Secondary surveillance radar; Relation (database); Tracking (education); Data mining; Real world data; Simulation; Artificial intelligence; Telecommunications","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":[0.001127683,0.0001616707,0.0001323052,0.0002298188,0.0001950128,0.0003897823,0.0006566111,0.00009197396,0.0003569956],"category_scores_gemma":[0.0002484382,0.0001479754,0.00007708003,0.0001611398,0.00002253921,0.003516226,0.0001291508,0.0001426455,0.0002842108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001138637,"about_ca_system_score_gemma":0.00007243872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001307843,"about_ca_topic_score_gemma":0.000001919231,"domain_scores_codex":[0.998189,0.00006868606,0.0004423663,0.0001903732,0.0008202445,0.0002893308],"domain_scores_gemma":[0.9983743,0.0001418139,0.0002596699,0.0003595632,0.0007579424,0.0001066668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001413371,0.0001353035,0.001638125,0.00002444727,0.00003246,2.87829e-7,0.002262919,0.001160757,0.00150282,0.4893746,0.01021232,0.4935146],"study_design_scores_gemma":[0.001529324,0.0001402679,0.01212418,0.0001093155,0.000008067001,0.00001884396,0.0002021487,0.8577338,0.00266059,0.003120331,0.1218932,0.0004599078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04890685,0.00005825228,0.894833,0.002190517,0.007329364,0.0008318718,0.00009712691,0.0003330175,0.04542],"genre_scores_gemma":[0.9776191,0.00003867846,0.0208265,0.0006099562,0.0003294673,0.00009231053,0.0003670232,0.000006651465,0.0001103703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9287122,"threshold_uncertainty_score":0.6034263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06809535940427182,"score_gpt":0.320704014594936,"score_spread":0.2526086551906642,"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."}}