{"id":"W2057500293","doi":"10.1364/ao.43.000403","title":"Target detection and recognition improvements by use of spatiotemporal fusion","year":2004,"lang":"en","type":"article","venue":"Applied Optics","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Clutter; Artificial intelligence; Thresholding; Computer science; Pixel; Noise (video); Computer vision; Constant false alarm rate; Gaussian noise; Sensor fusion; Pattern recognition (psychology); Object detection; False alarm; Radar; Image (mathematics); 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":[0.0001168491,0.0001044525,0.0001165448,0.00007099139,0.00004005714,0.0000190642,0.00003128619,0.0001136766,0.000008080038],"category_scores_gemma":[0.00004123605,0.0001144206,0.00001770251,0.0001079759,0.00003019321,0.0001284335,0.00002096597,0.0001142154,0.000006529541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004920315,"about_ca_system_score_gemma":0.000005054113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001640248,"about_ca_topic_score_gemma":0.000002594491,"domain_scores_codex":[0.9994794,0.00001010802,0.0001746041,0.0001136847,0.0000980069,0.000124165],"domain_scores_gemma":[0.9997361,0.00004268648,0.00005190296,0.000103391,0.00003530614,0.00003056592],"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":[0.00002176313,0.00001248919,0.00002582764,0.00004915272,0.00001737201,3.729351e-7,0.00009768853,0.009696367,0.954219,0.00005433317,0.00002776541,0.03577789],"study_design_scores_gemma":[0.0004126966,0.00006012954,0.0002425961,0.000007332773,0.00001187131,0.000001601278,0.00005926566,0.003067873,0.9900788,0.00571487,0.0002143846,0.0001285995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7929467,0.00002622499,0.2061952,0.000004708476,0.0001927948,0.0001760442,0.00002253705,0.0001656065,0.0002701877],"genre_scores_gemma":[0.7075055,0.00007698952,0.2923031,0.00001742744,0.00002448834,0.00001800445,0.00002598763,0.00002004132,0.000008448484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08610792,"threshold_uncertainty_score":0.4665938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993193696345349,"score_gpt":0.2221861161029369,"score_spread":0.1922541791394834,"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."}}