{"id":"W1975158286","doi":"10.1016/j.rse.2011.08.006","title":"Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":140,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Oak Ridge National Laboratory","keywords":"Primary production; Photosynthetically active radiation; Environmental science; Remote sensing; Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Vegetation (pathology); Leaf area index; Biome; Calibration; Evergreen; Mean squared error; Atmospheric sciences; Ecosystem; Satellite; Normalized Difference Vegetation Index; Vegetation Index; Mathematics; Geography; Statistics; Ecology; 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":[0.002743593,0.001133185,0.0006585958,0.0006659486,0.0003345751,0.000793486,0.0008795845,0.0009692794,0.0004393939],"category_scores_gemma":[0.004568653,0.0006994795,0.0006334966,0.0005242352,0.0003732071,0.0008632458,0.0004081064,0.0007880117,0.0002531062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000791794,"about_ca_system_score_gemma":0.0008342217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009680213,"about_ca_topic_score_gemma":0.007858283,"domain_scores_codex":[0.9995598,0.0001594873,0.00002445315,0.0001219669,0.000102955,0.00003132696],"domain_scores_gemma":[0.9969332,0.002378867,0.0001216766,0.0001795693,0.0003222943,0.00006427414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006429197,0.0003935865,0.04602927,0.00005623757,0.0002142596,0.00004036442,0.00005200907,0.8585567,0.01107888,0.000303797,0.0002602051,0.08237187],"study_design_scores_gemma":[0.00004111385,0.00006111242,0.00860428,0.00000202064,0.0000221367,0.000009873772,0.000006499327,0.9874373,0.00359074,0.0001619184,0.00005276445,0.0000101085],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9609835,0.0002335854,0.03740347,0.00004953705,0.0000147589,0.00004187229,0.0001716625,0.0004073864,0.000694301],"genre_scores_gemma":[0.983823,0.00009598235,0.01549275,0.00000795392,0.000006777219,0.00002661757,0.0002690059,0.00002694662,0.000250943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009680213,"threshold_uncertainty_score":0.01924771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01046097095508579,"score_gpt":0.1925808320690756,"score_spread":0.1821198611139898,"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."}}