{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01211788,0.001325189,0.001053722,0.004368604,0.001184528,0.002866389,0.001374019,0.001102307,0.00248782],"category_scores_gemma":[0.04738354,0.0003585853,0.0007213981,0.002964112,0.0008595429,0.002310015,0.0023787,0.001497626,0.0008989756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648418,"about_ca_system_score_gemma":0.002335539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002772694,"about_ca_topic_score_gemma":0.001969513,"domain_scores_codex":[0.9877494,0.003801208,0.001207176,0.0009599457,0.005855766,0.0004265305],"domain_scores_gemma":[0.9689072,0.01101149,0.005523621,0.003656059,0.01010384,0.0007977909],"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":[0.0007401286,0.0005476855,0.08799836,0.0005490049,0.0003353997,0.0003781267,0.0005686264,0.1663262,0.03144573,0.03909506,0.01799196,0.6540237],"study_design_scores_gemma":[0.00009259082,0.002355894,0.04134069,0.0001829529,0.0001191299,0.0009639458,0.0004023278,0.9013522,0.0235271,0.01273905,0.01669587,0.0002282362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04242966,0.0002316277,0.9441316,0.000250521,0.00009635261,0.0008957335,0.0008401127,0.002616202,0.008508366],"genre_scores_gemma":[0.4927374,0.0001330536,0.5024635,0.0001084126,0.00008047587,0.001099866,0.001373122,0.0003223899,0.001681791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01211788,"threshold_uncertainty_score":0.0640862,"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."}}