{"id":"W2102158737","doi":"10.1109/ijcnn.2003.1223771","title":"On the efficiency of OLS reduced probabilistic neural networks for aircraft-flare discrimination","year":2004,"lang":"en","type":"article","venue":"","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Probabilistic logic; Artificial neural network; Reduction (mathematics); Probabilistic neural network; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Time delay neural network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002988886,0.0001033552,0.0001130402,0.00005000527,0.00006771999,0.00001489807,0.0001331488,0.00006373659,0.00002336753],"category_scores_gemma":[0.0008513011,0.00006749335,0.00006589587,0.0001594894,0.0000418281,0.00005590379,0.00001259098,0.0001131336,0.000002025633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006519892,"about_ca_system_score_gemma":0.000008986874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003267098,"about_ca_topic_score_gemma":0.000003083136,"domain_scores_codex":[0.9993848,0.00003965032,0.0001912317,0.0001131156,0.00009745394,0.000173751],"domain_scores_gemma":[0.9991469,0.0005458193,0.00003132799,0.0001951018,0.00006352185,0.0000173694],"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":[0.00001356742,0.00001262202,0.000001602048,0.00004322964,0.000006648474,1.967285e-7,0.0001650212,0.9708712,0.01026023,0.01691983,0.0001082478,0.001597646],"study_design_scores_gemma":[0.0003986739,0.0002699753,0.0005579448,0.00003741836,0.00002238914,0.000003571828,0.0003343189,0.6697342,0.2912575,0.03717915,0.00001683924,0.0001879433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4922711,0.00002723643,0.5054054,0.0002006219,0.0004546351,0.0005084568,0.000003553428,0.0002296636,0.0008993438],"genre_scores_gemma":[0.9811949,0.000002197798,0.01854143,0.00003942263,0.00005069145,0.0001059298,0.000004409479,0.0000176285,0.00004339897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4889238,"threshold_uncertainty_score":0.2752299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03823810729462281,"score_gpt":0.2671055353906356,"score_spread":0.2288674280960128,"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."}}