{"id":"W2982007079","doi":"10.3390/rs11202447","title":"Estimating Pasture Biomass and Canopy Height in Brazilian Savanna Using UAV Photogrammetry","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Universidade Federal de Mato Grosso do Sul; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Canopy; Panicum; Biomass (ecology); Environmental science; Photogrammetry; Hectare; Forage; Remote sensing; Forestry; Biome; Geography; Agronomy; Ecology; Biology; Ecosystem","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":[0.0003177513,0.0002963018,0.0002194243,0.001093509,0.0001938999,0.0002484617,0.0002323919,0.000160194,0.000343593],"category_scores_gemma":[0.0006287906,0.0001678665,0.0002370313,0.0007304094,0.0001344771,0.0002883848,0.0002451405,0.00009164847,0.0001017108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002453115,"about_ca_system_score_gemma":0.0001630108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01318754,"about_ca_topic_score_gemma":0.04669815,"domain_scores_codex":[0.9998349,0.0000344485,0.00000936474,0.00004956947,0.00005289803,0.00001868717],"domain_scores_gemma":[0.9997261,0.00008826892,0.00007523103,0.00003221102,0.00006421474,0.00001396464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001319809,0.00007106384,0.7021511,0.0002792593,0.0001264718,0.0002697602,0.0007710494,0.009917811,0.1523624,0.0003165284,0.0002010473,0.1334016],"study_design_scores_gemma":[0.000006103295,0.00009289596,0.9491801,0.00004586915,0.0000667559,0.0003013859,0.0006349486,0.03607266,0.01252121,0.0001420596,0.0009179177,0.00001801884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920237,0.0003806759,0.006330788,0.00001030283,0.000002106587,0.00001241284,0.0001622879,0.00004238361,0.001035223],"genre_scores_gemma":[0.9897183,0.0001695358,0.009796473,0.00000460589,9.966009e-7,0.000009810421,0.0001719068,0.000006142033,0.0001221787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01318754,"threshold_uncertainty_score":0.02622151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009478341725520164,"score_gpt":0.2435305914241414,"score_spread":0.2340522496986212,"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."}}