{"id":"W2969371053","doi":"10.3390/rs11171979","title":"Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto Mississauga; University of Toronto","keywords":"Hyperspectral imaging; Remote sensing; Partial least squares regression; Multispectral image; Vegetation (pathology); Atmospheric radiative transfer codes; Random forest; Empirical modelling; Linear regression; Environmental science; Regression analysis; Mean squared error; Mathematics; Radiative transfer; Computer science; Geography; Statistics; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006211384,0.0003290009,0.0003724095,0.0000566741,0.0005344303,0.0001509571,0.00007822457,0.0001455931,0.00001880481],"category_scores_gemma":[0.000244204,0.0002613921,0.0001315986,0.000217881,0.0001608804,0.0002754569,0.00006469894,0.00041003,0.00001922531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003394911,"about_ca_system_score_gemma":0.00002953312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002783751,"about_ca_topic_score_gemma":0.00001258979,"domain_scores_codex":[0.9976919,0.000212785,0.000380899,0.0007027287,0.0005236344,0.0004880466],"domain_scores_gemma":[0.9991436,0.0003142344,0.0001633386,0.0001586278,0.00007664599,0.000143549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005607322,0.000009143237,0.0007040405,0.00004002532,0.00001939568,0.000005627525,0.00158642,0.6154586,0.3217669,0.000001973214,0.000005440127,0.06034632],"study_design_scores_gemma":[0.0008303323,0.0001419393,0.0007711572,0.000342475,0.00006970608,0.0001300786,0.0002652867,0.987126,0.009683638,0.0002366385,0.00004985565,0.0003529049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7019588,0.0002086874,0.2967403,0.0001591985,0.0001792138,0.0004650737,0.000001842886,0.00006445604,0.0002223867],"genre_scores_gemma":[0.5448878,0.00000922965,0.4547729,0.00005970756,0.0001104948,2.108354e-8,0.00001023178,0.00003298931,0.0001165976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3716674,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06502586134068847,"score_gpt":0.3224256337688589,"score_spread":0.2573997724281704,"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."}}