{"id":"W2462610241","doi":"10.1111/gcb.13388","title":"Allometric equations for integrating remote sensing imagery into forest monitoring programmes","year":2016,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Forest ecology and management","field":"Environmental Science","cited_by":408,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Regina; University of British Columbia; Queen's University; University of Toronto","funders":"Natural Environment Research Council; Smithsonian Conservation Biology Institute; Empresa Brasileira de Pesquisa Agropecuária; Agence Nationale de la Recherche; Ministry of Natural Resources; U.S. Forest Service; European Commission; United States Agency for International Development; Smithsonian Institution; U.S. Department of Agriculture; Sight Research UK; U.S. Department of State","keywords":"Allometry; Crown (dentistry); Tree allometry; Biomass (ecology); Remote sensing; Forest inventory; Environmental science; Tree (set theory); Vegetation (pathology); Ecology; Physical geography; Computer science; Forest management; Geography; Agroforestry; Mathematics; Biology; Biomass partitioning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003360507,0.001108548,0.0007146039,0.001900281,0.0003963528,0.001089848,0.00174156,0.0008145537,0.00309607],"category_scores_gemma":[0.01800613,0.0006321619,0.001133189,0.002798697,0.0006000352,0.002597519,0.001015071,0.002004534,0.001378936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001485883,"about_ca_system_score_gemma":0.0008869498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009426169,"about_ca_topic_score_gemma":0.009906105,"domain_scores_codex":[0.9982616,0.0007093456,0.0001190579,0.0003120658,0.0005202996,0.00007768812],"domain_scores_gemma":[0.9965386,0.0018499,0.0005831374,0.000428194,0.0005242325,0.0000758585],"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":[0.00003872659,0.0001332492,0.03056178,0.0001480991,0.000221807,0.0001041757,0.0002668241,0.6402588,0.002361161,0.06272344,0.008553913,0.254628],"study_design_scores_gemma":[0.000009545457,0.00003225428,0.01396198,0.00004010242,0.00004205921,0.00008998682,0.00003879653,0.9389663,0.0006055057,0.03692748,0.009239694,0.00004628014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01319326,0.0003638596,0.9814804,0.0002660399,0.00006533194,0.0001203579,0.001083306,0.0007276113,0.002699842],"genre_scores_gemma":[0.213272,0.001099391,0.7755386,0.0002763126,0.0001836082,0.0007266909,0.003247452,0.0005749547,0.005080986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009426169,"threshold_uncertainty_score":0.01874262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04226482336502861,"score_gpt":0.3045956985590102,"score_spread":0.2623308751939816,"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."}}