{"id":"W4281632006","doi":"10.1117/12.2630821","title":"Infrared scene capabilities of ShipIR (v4.2)","year":2022,"lang":"en","type":"article","venue":"","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BGC Engineering (Canada)","funders":"","keywords":"Rendering (computer graphics); Computer science; Computer graphics (images); Computer vision; Frame rate; Image resolution; Artificial intelligence; Frame (networking); 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003374846,0.00008399356,0.0001477223,0.0001263972,0.00006700978,0.000006046745,0.0001513007,0.00002982488,0.003589367],"category_scores_gemma":[0.000137129,0.00008870799,0.00005535,0.0002204,0.00003936954,0.00006904405,0.0000831922,0.0001673828,0.000009593809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007884969,"about_ca_system_score_gemma":0.00001493842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000111223,"about_ca_topic_score_gemma":0.000001209369,"domain_scores_codex":[0.999292,0.00009690942,0.0002057089,0.00009046144,0.0001589395,0.0001559913],"domain_scores_gemma":[0.9995164,0.0001902172,0.00002101487,0.0002230748,0.00002710521,0.00002221121],"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":[0.00008169888,0.00006581954,0.003059601,0.0004709645,0.0001878081,0.00001171852,0.005626795,0.6990104,0.2354104,0.01322256,0.02832409,0.01452813],"study_design_scores_gemma":[0.0004047219,0.0001713185,0.002500079,0.000004651888,0.00001288563,0.00002148393,0.007576248,0.01418711,0.9156808,0.02972917,0.02933622,0.0003752619],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8244179,0.000358346,0.008909396,0.00004264704,0.001473866,0.0001865009,0.0000423725,0.001268218,0.1633008],"genre_scores_gemma":[0.8718508,0.00001277881,0.1214385,0.00006801758,0.00005000236,0.0001001788,0.00000618012,0.00003463649,0.006438821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6848233,"threshold_uncertainty_score":0.9973215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02336698373786505,"score_gpt":0.2277104161338175,"score_spread":0.2043434323959525,"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."}}