{"id":"W2127411854","doi":"10.1109/igarss.2007.4423533","title":"Impact of spectrally dependent gain errors in hyperspectral data on the determination of chlorophyll concentrations in vegetation","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Canadian Space Agency","keywords":"MODTRAN; Remote sensing; Hyperspectral imaging; Radiance; Red edge; Radiometric calibration; Calibration; Lambda; Algorithm; Computer science; Artificial intelligence; Mathematics; Physics; Optics; Geology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004507913,0.0004378907,0.0002727609,0.0004694895,0.000394431,0.0005089966,0.0003977377,0.000736124,0.0003452818],"category_scores_gemma":[0.01947495,0.0002636322,0.0004681833,0.0007113601,0.0006625316,0.0006674592,0.0005564465,0.0004365206,0.0001043555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006935525,"about_ca_system_score_gemma":0.0003378201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004007746,"about_ca_topic_score_gemma":0.005124227,"domain_scores_codex":[0.9971243,0.001001465,0.0001511432,0.0005757419,0.001010763,0.0001366452],"domain_scores_gemma":[0.9866348,0.00977691,0.00105559,0.001141796,0.001292475,0.00009839922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003564288,0.0005967494,0.1263293,0.0002439218,0.0003607557,0.0005218589,0.000570658,0.5710067,0.2092575,0.001437025,0.00040101,0.08571024],"study_design_scores_gemma":[0.00008290607,0.0009470618,0.1383972,0.00004376829,0.0001270485,0.0005750498,0.0002289693,0.457202,0.4005394,0.0008233847,0.0009319505,0.0001012644],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711961,0.0001129122,0.02752085,0.00007446772,0.0000167398,0.00004246881,0.0001321853,0.0001658603,0.0007384249],"genre_scores_gemma":[0.9795209,0.00005543874,0.01982926,0.00005118524,0.000004631861,0.00002389939,0.0002217687,0.00004797068,0.0002448488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004507913,"threshold_uncertainty_score":0.02384043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02007773778896785,"score_gpt":0.2807089129539688,"score_spread":0.260631175165001,"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."}}