{"id":"W2956043127","doi":"10.3390/rs11131572","title":"Retrieving Leaf Chlorophyll Content by Incorporating Variable Leaf Surface Reflectance in the PROSPECT Model","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Nanjing University; Government of Jiangsu Province; China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Chlorophyll; Reflectivity; Leaf area index; Canopy; Coefficient of determination; Environmental science; Remote sensing; Chlorophyll a; Mean squared error; Botany; Horticulture; Materials science; Mathematics; Optics; Geology; Biology; Physics","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":[0.0004551632,0.000700058,0.000513365,0.0005015709,0.0001577392,0.0005438474,0.0006588763,0.0007059581,0.0005256585],"category_scores_gemma":[0.0006652356,0.0004083871,0.0009640308,0.0004918848,0.0002067061,0.001041621,0.0005134465,0.0005054462,0.0003458706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003956167,"about_ca_system_score_gemma":0.0005548899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004930968,"about_ca_topic_score_gemma":0.004002118,"domain_scores_codex":[0.9998411,0.00003414254,0.000007855122,0.00004672634,0.00005123302,0.00001893481],"domain_scores_gemma":[0.9998589,0.00005383976,0.00001605465,0.00001977489,0.00004249893,0.000008863372],"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":[0.0001689764,0.00009639228,0.01043469,0.0001039997,0.00007119081,0.0001823345,0.0001047916,0.8799919,0.05316439,0.001191166,0.0006741583,0.05381609],"study_design_scores_gemma":[0.000007266973,0.0000139884,0.0008479029,0.000001878343,0.000006242707,0.00002365334,0.000006751012,0.9972628,0.001428607,0.0001900165,0.0002002823,0.0000105628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4825234,0.0002857852,0.5117191,0.0001866038,0.00003571434,0.00007380203,0.0005901174,0.002212328,0.002373191],"genre_scores_gemma":[0.8821605,0.0002285389,0.1151241,0.00007423081,0.00001308246,0.0001013942,0.0009111484,0.0001499997,0.001237011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004930968,"threshold_uncertainty_score":0.009804547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685333932544451,"score_gpt":0.2175438905546024,"score_spread":0.2006905512291579,"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."}}