{"id":"W4213235672","doi":"10.1093/forestry/cpab051","title":"Developing a forest inventory approach using airborne single photon lidar data: from ground plot selection to forest attribute prediction","year":2021,"lang":"en","type":"article","venue":"Forestry An International Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Natural Resources Canada; Ontario Forest Research Institute; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Forest inventory; Basal area; Lidar; Environmental science; Taiga; Sampling (signal processing); Remote sensing; Forestry; Range (aeronautics); Forest management; Agroforestry; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001748571,0.0002189048,0.0002622164,0.0003304823,0.0004361662,0.0004273125,0.001390191,0.0001752309,0.0001414136],"category_scores_gemma":[0.0006508733,0.0002181202,0.0001058522,0.0008977348,0.0002372323,0.00160969,0.0008014077,0.0007706034,0.0000683335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001514779,"about_ca_system_score_gemma":0.0004473191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00252078,"about_ca_topic_score_gemma":0.002634132,"domain_scores_codex":[0.9957387,0.0003627137,0.0007265819,0.0006538864,0.001998566,0.0005195374],"domain_scores_gemma":[0.9978089,0.0001850063,0.0003021874,0.0006095674,0.0007115455,0.0003827731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003530173,0.0008928563,0.9105713,0.00001818467,0.0002399053,0.0001486708,0.0005923155,0.06879096,0.0132728,0.001012014,0.001616151,0.002491821],"study_design_scores_gemma":[0.0008141499,0.0003531067,0.8820931,0.0002309163,0.00004438951,0.0007085997,0.0005776466,0.09111328,0.003521997,0.006361529,0.01388314,0.0002981022],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216508,0.0000369685,0.0762956,0.0004144634,0.0004695432,0.0002527423,0.0001105822,0.00003129112,0.0007380235],"genre_scores_gemma":[0.9653044,0.00003385328,0.0324071,0.00008602409,0.001259908,0.000006299797,0.0006810638,0.00004393018,0.0001774658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04388849,"threshold_uncertainty_score":0.8894685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1747286442217567,"score_gpt":0.3765935387148191,"score_spread":0.2018648944930624,"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."}}