{"id":"W2124310963","doi":"10.1109/igarss.2006.465","title":"Data Fusion: Cumulative Effects of Discrete Fusion on Target Detection Probability","year":2006,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Centre For Cold Ocean Resources Engineering","funders":"","keywords":"Sensor fusion; Computer science; Bayesian probability; Statistical power; Data mining; Artificial intelligence; Statistics; Mathematics","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":[0.01040943,0.001068897,0.001351076,0.001786681,0.0008429411,0.002895708,0.001662927,0.001737762,0.001988335],"category_scores_gemma":[0.06577663,0.0009539947,0.001122809,0.002324965,0.003110803,0.005247795,0.004580612,0.002773819,0.0004768687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001778622,"about_ca_system_score_gemma":0.001048126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001378653,"about_ca_topic_score_gemma":0.001040556,"domain_scores_codex":[0.9922978,0.002352197,0.0003497589,0.001589644,0.00300455,0.0004059068],"domain_scores_gemma":[0.9116834,0.07148951,0.003859622,0.007991664,0.004286884,0.0006888487],"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":[0.0007451592,0.0001379075,0.01663781,0.0006335879,0.0003681149,0.0003064828,0.0005476051,0.6305828,0.01612586,0.07735232,0.001435417,0.255127],"study_design_scores_gemma":[0.00004038756,0.0003175098,0.007876482,0.00008308491,0.0002280832,0.0004737204,0.00008983509,0.8955565,0.01754749,0.07481015,0.002861673,0.0001150647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04150661,0.001415392,0.9528191,0.0004763068,0.00008418739,0.00006683312,0.0001235104,0.0004312765,0.003076812],"genre_scores_gemma":[0.8511823,0.001436,0.1449212,0.0002494259,0.0002625849,0.0001581787,0.0002382828,0.0002182726,0.001333719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01040943,"threshold_uncertainty_score":0.05505097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133125531305815,"score_gpt":0.2577583247794213,"score_spread":0.2364270694663631,"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."}}