{"id":"W4386260296","doi":"10.1109/crv60082.2023.00040","title":"Enhancing Satellite Trail Detection in Night Sky Imagery with Automatic Salience Thresholding","year":2023,"lang":"en","type":"article","venue":"","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Thresholding; Salience (neuroscience); Computer science; Satellite; Satellite imagery; Artificial intelligence; Remote sensing; Sky; Computer vision; Meteorology; Geography; Image (mathematics); Astronomy","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":[0.0005065775,0.0004583723,0.0004799366,0.001631588,0.0002862686,0.0007080339,0.0005719257,0.0003456043,0.0008779968],"category_scores_gemma":[0.001352446,0.0003062011,0.0004375414,0.0007548259,0.0004050601,0.0008458021,0.0006607437,0.0004291858,0.0005923691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002153174,"about_ca_system_score_gemma":0.0004694609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001366159,"about_ca_topic_score_gemma":0.002898283,"domain_scores_codex":[0.9996461,0.00004500105,0.00001774678,0.00005819771,0.0001875556,0.00004540083],"domain_scores_gemma":[0.9993322,0.0001922922,0.0001127101,0.0001065531,0.0002192867,0.0000369062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003436005,0.0001225557,0.007486305,0.0003880891,0.00008966412,0.0002631315,0.0003101485,0.01768818,0.4685607,0.002752957,0.002412932,0.4995818],"study_design_scores_gemma":[0.0000613218,0.0003589259,0.03141029,0.00006320154,0.000127924,0.001453598,0.0002748258,0.5638648,0.3850175,0.00484609,0.01243223,0.00008914954],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1297771,0.0004058249,0.8660746,0.00008270211,0.00005941921,0.00007166764,0.0001053067,0.001267192,0.002156138],"genre_scores_gemma":[0.3974202,0.0004093249,0.5997614,0.00007460171,0.00005305286,0.00004159717,0.0004144763,0.0002375102,0.001587868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001631588,"threshold_uncertainty_score":0.002937198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208765379396507,"score_gpt":0.2485020562867111,"score_spread":0.2264144024927461,"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."}}