{"id":"W2942962169","doi":"10.1109/jstars.2019.2910558","title":"Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Multispectral image; Remote sensing; Mean squared error; Vegetation (pathology); Artificial intelligence; Computer science; Multispectral pattern recognition; Environmental science; Mathematics; Statistics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002720072,0.0000907348,0.000182105,0.00003929741,0.0001153544,0.00003707758,0.0000563612,0.00004682028,6.840164e-7],"category_scores_gemma":[0.00004775963,0.00005897767,0.00002177877,0.0002165384,0.00009518213,0.0001193155,0.00001706558,0.0002095673,2.861196e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003943055,"about_ca_system_score_gemma":0.00001796683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005458987,"about_ca_topic_score_gemma":0.00008766526,"domain_scores_codex":[0.9992384,0.00002277755,0.0003169473,0.0001095176,0.0001729115,0.0001394617],"domain_scores_gemma":[0.999519,0.00006987564,0.0002371948,0.00007061828,0.00007892809,0.00002436601],"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":[0.0000445903,0.00001384392,0.01461338,0.00009986198,0.00002001652,8.632355e-7,0.001751223,0.09953661,0.8347324,0.00003306389,0.00001596753,0.04913815],"study_design_scores_gemma":[0.000393552,0.00006019888,0.2880706,0.0001607529,0.00001553061,0.0000610819,0.0001596972,0.671243,0.03961862,0.0001069732,0.00003311435,0.0000768309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963598,0.00006560533,0.00275003,0.0002436089,0.0001276554,0.0002466838,2.39049e-7,0.000005887537,0.0002004873],"genre_scores_gemma":[0.7447232,0.00003235634,0.2551227,0.00002076518,0.00006666064,2.449428e-8,4.05922e-7,0.000005168604,0.00002877448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7951138,"threshold_uncertainty_score":0.240504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997800694758447,"score_gpt":0.2116476444064835,"score_spread":0.1916696374588991,"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."}}