{"id":"W2050293962","doi":"10.1117/12.736476","title":"<title>FTIR-based airborne spectral imagery for target interrogation</title>","year":2007,"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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Remote sensing; Detector; Broadband; Computer science; Optics; Spectral resolution; Interferometry; Spectral bands; Fourier transform spectroscopy; Interrogation; Calibration; Infrared; Physics; Geology; Spectral line","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":[0.0006058984,0.0005011774,0.0004320767,0.001385268,0.0006897627,0.001124311,0.0009264708,0.0006514663,0.2134088],"category_scores_gemma":[0.0004663103,0.0002169578,0.0002554554,0.00127503,0.0003877248,0.00105427,0.0006264449,0.000774461,0.1052137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009340988,"about_ca_system_score_gemma":0.0006447161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003190546,"about_ca_topic_score_gemma":0.008083378,"domain_scores_codex":[0.999647,0.00002424772,0.000009982335,0.00007284991,0.0002084554,0.00003741725],"domain_scores_gemma":[0.9993119,0.00006380797,0.00003497682,0.00008278528,0.0004228942,0.00008359777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001173584,0.00006061726,0.000839658,0.0002194707,0.000009350479,0.0001546895,0.00007295309,0.0004436021,0.06049052,0.009522828,0.5536678,0.3744011],"study_design_scores_gemma":[0.00001186706,0.00007494301,0.001346483,0.00003151659,0.000003351647,0.0001297477,0.00003365133,0.001393422,0.02597142,0.0007093612,0.9702799,0.00001432941],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01268572,0.004851824,0.08548425,0.005652835,0.006875924,0.0006318013,0.006690018,0.01366841,0.8634593],"genre_scores_gemma":[0.02658353,0.002419141,0.03298119,0.0005125147,0.0005506472,0.00009962464,0.003644121,0.00239839,0.9308107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2134088,"threshold_uncertainty_score":0.7139235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673398592224118,"score_gpt":0.2460548777988085,"score_spread":0.2293208918765673,"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."}}