{"id":"W2348715555","doi":"","title":"A Pricisely Tracking Method of IR Weak Target","year":2005,"lang":"en","type":"article","venue":"Laser & Infrared","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Channel (broadcasting); Filter (signal processing); Tracking (education); Computer science; Frame (networking); Noise (video); Enhanced Data Rates for GSM Evolution; Algorithm; Computer vision; Telecommunications; Image (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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006166341,0.000241653,0.0003922684,0.0002329656,0.00005651545,0.00002997389,0.0002717821,0.0001924799,0.000613516],"category_scores_gemma":[0.0003557906,0.0002477457,0.0001526814,0.0004321967,0.00003935665,0.0003743947,0.00004836559,0.0003124324,0.00009425384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008936495,"about_ca_system_score_gemma":0.00002583557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005164759,"about_ca_topic_score_gemma":0.000003112649,"domain_scores_codex":[0.9984767,0.0001549935,0.0005116934,0.0002274003,0.0002391042,0.0003900434],"domain_scores_gemma":[0.9989485,0.0003611984,0.0000953486,0.0004114501,0.000102837,0.00008065016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001033758,0.00008699873,0.0009778345,0.0003059717,0.0002899854,0.00001583719,0.002706431,0.576456,0.305212,0.0008790449,0.01542299,0.09754355],"study_design_scores_gemma":[0.0004596824,0.0000426538,0.002022679,0.0000294977,0.00002569753,0.00001838534,0.0002142713,0.02211839,0.8913248,0.002872435,0.08054996,0.0003215736],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.169946,0.0009210907,0.715503,0.0001393157,0.001311281,0.0005160868,0.00007861669,0.002227592,0.109357],"genre_scores_gemma":[0.09249657,0.00003325062,0.9056522,0.0001092867,0.000242109,0.00004129414,0.00001038401,0.00006800805,0.001346874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5861128,"threshold_uncertainty_score":0.9999975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601168908597728,"score_gpt":0.2762253857416611,"score_spread":0.2502136966556838,"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."}}