{"id":"W1977003511","doi":"10.1117/12.2049803","title":"Passive signatures concealed objects recorded by multispectral and hyperspectral systems in visible, infrared and terahertz range","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada)","funders":"","keywords":"Multispectral image; Hyperspectral imaging; Spectroradiometer; Remote sensing; Spectral signature; Infrared; Computer science; Terahertz radiation; Explosive material; Microbolometer; Computer vision; Artificial intelligence; Range (aeronautics); Thermography; Optics; Materials science; Detector; Geology; Geography; Physics; Bolometer; Telecommunications; Reflectivity","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":[0.0005890186,0.0003516972,0.0005341501,0.0001590256,0.00006035763,0.0001435458,0.0003888842,0.0002915452,0.000004074264],"category_scores_gemma":[0.0008804895,0.0003130131,0.0001965119,0.0002679037,0.0002062308,0.0004848132,0.00007999799,0.0004671473,3.468361e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001525551,"about_ca_system_score_gemma":0.00001126354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001857283,"about_ca_topic_score_gemma":4.162685e-7,"domain_scores_codex":[0.9982555,9.281366e-8,0.0005936213,0.0003660043,0.0003590751,0.0004257213],"domain_scores_gemma":[0.9987884,0.0003926076,0.0001867882,0.0000503298,0.0004695029,0.0001123531],"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":[0.0001097808,0.0000346892,0.0007065021,0.0008086204,0.000288237,2.13169e-7,0.0006591834,0.0008914837,0.9316656,0.06218069,0.00193914,0.0007158423],"study_design_scores_gemma":[0.005529981,0.0008609305,0.008500328,0.0008291918,0.0001910913,0.00006343542,0.005490949,0.4494827,0.5204754,0.004673372,0.002590865,0.001311715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963905,0.0008355228,0.0000889001,0.0002796979,0.0004006617,0.0005807938,0.00004218952,0.0001878046,0.001193936],"genre_scores_gemma":[0.8545547,0.0004586011,0.1442162,0.00005599594,0.0003027271,0.0002124689,0.000006558686,0.00008705129,0.0001057412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4485913,"threshold_uncertainty_score":0.9999322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009859830166602124,"score_gpt":0.223168696309379,"score_spread":0.2133088661427769,"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."}}