{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001980101,0.0001432,0.0002085101,0.0001786793,0.0001013001,0.00001477707,0.00007583841,0.0000259477,0.00001552853],"category_scores_gemma":[0.00009813817,0.0001686288,0.0001046046,0.000215852,0.00004357045,0.00006316262,0.00002748724,0.0001898851,0.000002485742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003682539,"about_ca_system_score_gemma":0.00001165862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001048606,"about_ca_topic_score_gemma":0.000002863053,"domain_scores_codex":[0.9991188,0.00005003599,0.0002188387,0.0002054471,0.0001603871,0.0002464685],"domain_scores_gemma":[0.9994646,0.0001638703,0.0000405782,0.0002341944,0.0000577135,0.00003902989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009658722,0.00004653215,0.0005414236,0.0001790382,0.000131135,0.00004422109,0.00260366,0.2300133,0.6917719,0.0001074343,0.0004135448,0.07405125],"study_design_scores_gemma":[0.0003571619,0.00004499919,0.005702502,0.00001834386,0.00004011102,0.00004589963,0.0008804134,0.9667011,0.02544179,0.0001542778,0.0004201845,0.0001932056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855338,0.0002784607,0.01133301,0.00007851287,0.0005006418,0.0006313106,0.00005425889,0.0002611811,0.001328769],"genre_scores_gemma":[0.9923129,0.00001257011,0.007272957,0.0000131878,0.00005601197,0.00002857898,0.00003480039,0.0000674523,0.0002015608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7366878,"threshold_uncertainty_score":0.6876482,"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."}}