{"id":"W2037615034","doi":"10.1016/j.rse.2011.11.012","title":"Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery","year":2011,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Environment and Climate Change Canada; Centre de Géomatique du Québec; McMaster University; University of Toronto","funders":"Oak Ridge National Laboratory; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Photosynthetically active radiation; Environmental science; Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Temperate forest; Remote sensing; Taiga; Canopy; Temperate rainforest; Spectroradiometer; Vegetation (pathology); Temperate climate; Boreal; Evergreen; Mean squared error; Leaf area index; Atmospheric sciences; Ecosystem; Normalized Difference Vegetation Index; Reflectivity; Forestry; Geography; Mathematics; Ecology; Statistics; Vegetation Index; Geology","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":[0.0002763051,0.0003862185,0.0006660759,0.0001561277,0.00008659563,0.00001382683,0.0001588681,0.0001411886,0.00001606471],"category_scores_gemma":[0.00009156866,0.0003408889,0.0001287805,0.0004262687,0.0008580583,0.0002084192,0.0003186809,0.0002448618,0.000006261604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003041617,"about_ca_system_score_gemma":0.00002498788,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01889851,"about_ca_topic_score_gemma":0.001001599,"domain_scores_codex":[0.9971574,0.0001897987,0.0008841479,0.0006426014,0.0006136236,0.0005124202],"domain_scores_gemma":[0.998403,0.00008390762,0.0006683861,0.0006666534,0.00002136856,0.000156718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000168958,0.0001685738,0.03913385,0.0001100571,0.0000602459,0.0001565687,0.004825992,0.03101458,0.6585899,0.000001010886,0.00004621519,0.2657241],"study_design_scores_gemma":[0.0005329986,0.0001949065,0.6107224,0.0003812065,0.00009098474,0.0001297932,0.0001609895,0.2502353,0.1368401,0.0000960541,0.0001128757,0.0005023152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989254,0.00004926643,0.009404392,0.00003733771,0.00007804982,0.0003514788,0.00001102534,0.00001741946,0.0007970578],"genre_scores_gemma":[0.822179,0.0001393391,0.1775564,0.00002722819,0.00001630532,4.733711e-9,0.000006682546,0.00003613661,0.00003898239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5715886,"threshold_uncertainty_score":0.9999043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589477247780135,"score_gpt":0.2029738028266876,"score_spread":0.1870790303488863,"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."}}