{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002436202,0.0001919384,0.0002172659,0.0001028011,0.0001618385,0.00007600005,0.0000823284,0.0001184413,0.00004347725],"category_scores_gemma":[0.00003025494,0.0001897846,0.00004525768,0.0005685852,0.0001156261,0.0001137279,0.0001191594,0.0002220126,0.0001095742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001880862,"about_ca_system_score_gemma":0.0000136002,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00831571,"about_ca_topic_score_gemma":0.0008108988,"domain_scores_codex":[0.9985834,0.00006454002,0.0002474833,0.0004822652,0.0002216075,0.0004006615],"domain_scores_gemma":[0.9993578,0.00005427986,0.00009850044,0.0003623721,0.000008926552,0.0001181411],"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":[0.000009069729,0.00001454707,0.00962283,0.00002363955,0.000009192724,0.00002474085,0.000725899,0.003699905,0.4864492,0.000002603722,0.00007973804,0.4993386],"study_design_scores_gemma":[0.000288017,0.00001597253,0.01059889,0.0001129529,0.00001156204,0.0001544981,0.0001724395,0.9798512,0.006307245,0.0003230353,0.001887614,0.000276558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796261,0.00004767755,0.01378588,0.000198907,0.0001710412,0.0002670105,0.000001265429,0.00006127745,0.005840861],"genre_scores_gemma":[0.8493382,0.000002566819,0.1502992,0.0001520284,0.00005232204,4.243517e-9,0.000004890684,0.00002737331,0.0001233966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9761513,"threshold_uncertainty_score":0.998288,"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."}}