{"id":"W4214647988","doi":"10.3390/s22051817","title":"Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Computer science; Principal component analysis; Computer vision; Sorting; Support vector machine; Data set; Dynamic range; High dynamic range; Remote sensing; Pattern recognition (psychology); Algorithm; Geology","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":[0.0005424351,0.0005905694,0.0002835769,0.000707695,0.0001826047,0.0004897663,0.0005825884,0.0006010202,0.001609531],"category_scores_gemma":[0.00078296,0.0002944485,0.0003781196,0.0004102918,0.0003157044,0.001207497,0.0005398464,0.0006697949,0.0007052497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002496911,"about_ca_system_score_gemma":0.000167447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000271645,"about_ca_topic_score_gemma":0.0006997688,"domain_scores_codex":[0.9993677,0.0001101615,0.00002731457,0.0001547514,0.0002937447,0.00004635821],"domain_scores_gemma":[0.9992917,0.0002522825,0.0001175268,0.0001000363,0.0002122676,0.00002613947],"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":[0.0001459483,0.0001121633,0.001609082,0.0002727422,0.00004345921,0.000114714,0.0001277113,0.004684953,0.7494456,0.001138238,0.0008699136,0.2414354],"study_design_scores_gemma":[0.0000214276,0.0003574647,0.007045883,0.00003681492,0.00006160919,0.001022679,0.00009992113,0.1233215,0.8520293,0.0008491515,0.01507665,0.00007746162],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1606514,0.003125693,0.8285756,0.0002963936,0.0001112126,0.00009951177,0.0001146075,0.0012902,0.005735517],"genre_scores_gemma":[0.5348005,0.001641255,0.4586666,0.000369352,0.0001062958,0.0001090962,0.000233446,0.0001176014,0.003955781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001609531,"threshold_uncertainty_score":0.005384445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144463708852683,"score_gpt":0.2422989136972864,"score_spread":0.2308542766087596,"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."}}