{"id":"W4234169617","doi":"10.1117/12.833478","title":"Chemical agent standoff detection and identification with a hyperspectral imaging infrared sensor","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada)","funders":"","keywords":"Hyperspectral imaging; Chemical imaging; Remote sensing; Radiometric calibration; Spectrometer; Computer science; Calibration; Data acquisition; Imaging spectrometer; Imaging spectroscopy; Infrared; Image sensor; Optics; Artificial intelligence; Physics","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":[0.0002161162,0.0002554021,0.0003162973,0.0001012567,0.00008007567,0.000138278,0.0003751937,0.000122638,0.0000191451],"category_scores_gemma":[0.0002948538,0.0002090979,0.0002771856,0.0003609585,0.0001694833,0.0003925875,0.0000475821,0.0002693172,4.885998e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002085329,"about_ca_system_score_gemma":0.00001824057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000484159,"about_ca_topic_score_gemma":4.794304e-8,"domain_scores_codex":[0.9983562,6.129093e-9,0.000459556,0.0003754517,0.0005052948,0.0003035215],"domain_scores_gemma":[0.9986597,0.00006379286,0.0003070372,0.00005329917,0.000810532,0.0001056236],"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":[0.0001584776,0.00009623355,0.000391778,0.000201715,0.0002627419,1.383155e-7,0.000174471,0.0000107418,0.9698988,0.02816315,0.0002014313,0.0004403307],"study_design_scores_gemma":[0.0009131522,0.00009064819,0.0007277053,0.00008991434,0.00030062,0.00003981069,0.00221565,0.007067876,0.9872881,0.0007819445,0.000232403,0.0002522251],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958501,0.0001712866,0.00009471029,0.001040498,0.00003379367,0.0001634834,0.00001989521,0.00009232023,0.002533875],"genre_scores_gemma":[0.9740694,0.00007625949,0.02524981,0.00005694388,0.000231357,0.00002979789,0.000006185297,0.00003042414,0.0002497928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02738121,"threshold_uncertainty_score":0.8526765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007640972655028323,"score_gpt":0.2260876276612109,"score_spread":0.2184466550061826,"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."}}