{"id":"W4297103081","doi":"10.1016/j.rse.2022.113284","title":"Assessing a soil-removed semi-empirical model for estimating leaf chlorophyll content","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leaf area index; Vegetation (pathology); Canopy; Remote sensing; Generality; Environmental science; Empirical modelling; Robustness (evolution); Chlorophyll; Soil science; Computer science; Chemistry; Botany; Geology; Simulation","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":[0.002914914,0.0006381116,0.0005799488,0.0004457794,0.0004331818,0.0009045342,0.001689799,0.001707605,0.0005470763],"category_scores_gemma":[0.007685171,0.0004262965,0.0008092599,0.0004170392,0.0004082638,0.0009652844,0.0006017571,0.0007454779,0.0002176955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001309788,"about_ca_system_score_gemma":0.00138366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03197076,"about_ca_topic_score_gemma":0.0175022,"domain_scores_codex":[0.9995927,0.0002002627,0.00002330838,0.00008703614,0.00006200103,0.00003472938],"domain_scores_gemma":[0.9941532,0.004853939,0.0002345691,0.0002106634,0.0004563736,0.00009121643],"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":[0.00009414806,0.00006001778,0.003796214,0.00003156234,0.00005667914,0.00002478351,0.00002055541,0.9875728,0.001087733,0.0003495205,0.0001076387,0.006798465],"study_design_scores_gemma":[0.000006378621,0.000009816974,0.000532279,0.000001295496,0.000005895195,0.000003443447,0.000003948847,0.9991699,0.0001464986,0.00009944147,0.00001794015,0.000003148208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7864733,0.0002666332,0.2111069,0.0002882833,0.00003509813,0.00004669468,0.0002696605,0.0006161656,0.0008972384],"genre_scores_gemma":[0.9724084,0.00005990775,0.0264952,0.00005800476,0.00001404008,0.00003641818,0.0003207269,0.00006057429,0.0005467536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03197076,"threshold_uncertainty_score":0.06356937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05815446451247461,"score_gpt":0.2751296756853252,"score_spread":0.2169752111728506,"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."}}