{"id":"W2051750928","doi":"10.4028/www.scientific.net/amr.143-144.920","title":"A Novel Combination Method of Conflict Evidence in Multi-Sensor Target Recognition","year":2010,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Association of Emergency Physicians","funders":"","keywords":"Credibility; Proposition; Function (biology); Order (exchange); Computer science; Data mining; Artificial intelligence; Pattern recognition (psychology); Political science; Economics","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":[0.004246744,0.0001307919,0.0002784424,0.0003694584,0.0001112374,0.0001366188,0.000831467,0.0001459559,0.0001372424],"category_scores_gemma":[0.002106103,0.0001250696,0.00002995134,0.0007260559,0.0001190185,0.0008618691,0.0003435861,0.0004510893,0.00005981143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002977386,"about_ca_system_score_gemma":0.00006973593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003152024,"about_ca_topic_score_gemma":0.00005234555,"domain_scores_codex":[0.9974251,0.0004956572,0.0005028856,0.0005247633,0.0005917435,0.0004598785],"domain_scores_gemma":[0.9970267,0.00139129,0.0001503333,0.000697392,0.0006443866,0.00008988428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006596893,0.0001770583,0.00006951494,0.00004813115,0.000002820744,0.000006465098,0.0002526787,0.0001441299,0.9730964,0.002254869,0.00005766285,0.02382426],"study_design_scores_gemma":[0.001022864,0.0001233683,0.003143785,0.000219367,0.000001308283,0.00002066849,0.00004564098,0.008632272,0.9828663,0.00265099,0.001100115,0.0001733282],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3324779,0.00006445652,0.665257,0.0003370996,0.001051438,0.0005363662,0.00007255674,0.0000893172,0.0001138265],"genre_scores_gemma":[0.4258306,0.0001044667,0.5738571,0.00002497987,0.00003504197,0.00005202947,0.00002706677,0.00001116793,0.00005758737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09335268,"threshold_uncertainty_score":0.5100191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2163872089434315,"score_gpt":0.4393620270461432,"score_spread":0.2229748181027117,"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."}}