{"id":"W4385819592","doi":"10.1109/tgrs.2023.3304531","title":"Angular Effect Correction for Improved LAI and FVC Retrieval Using GF-1 Wide Field View Data","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Natural Science Foundation of China","keywords":"Leaf area index; Bidirectional reflectance distribution function; Remote sensing; GF(2); Mean squared error; Vegetation (pathology); Data set; Mathematics; Environmental science; Statistics; Physics; Reflectivity; Optics; Geography; Medicine; Agronomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006492545,0.0001999585,0.0002058969,0.00008607364,0.0008252382,0.0001225296,0.0001438731,0.0001397135,0.000006153028],"category_scores_gemma":[0.0001074406,0.0001577742,0.00005131199,0.0007717288,0.0002400124,0.0003610911,0.00001833952,0.0002340935,0.00001238161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007096067,"about_ca_system_score_gemma":0.0000150687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009469548,"about_ca_topic_score_gemma":0.0002856512,"domain_scores_codex":[0.9984266,0.00007665101,0.0001936655,0.0006980016,0.000228423,0.0003766396],"domain_scores_gemma":[0.9989613,0.0004281573,0.00007255719,0.0003996804,0.00001715693,0.0001211897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006536426,0.000009113017,0.000007702627,0.00002626117,0.00000976721,0.000008177121,0.0001887677,0.001207581,0.2029791,8.510623e-8,0.0003704572,0.7951276],"study_design_scores_gemma":[0.0002993939,0.0002286538,0.0005906157,0.0001407532,0.00006659389,0.0001774832,0.00009601724,0.9407735,0.05561957,0.0000477723,0.001718132,0.0002415464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4767857,0.00002813555,0.5204105,0.0005765053,0.001520284,0.0004695508,0.00001281735,0.000126281,0.00007020778],"genre_scores_gemma":[0.8718541,0.0008203831,0.1229659,0.001418933,0.0002420629,1.398517e-7,0.00002815756,0.00007335025,0.002597025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9395659,"threshold_uncertainty_score":0.6433845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002433519211898,"score_gpt":0.2667623550899454,"score_spread":0.2467380198978265,"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."}}