{"id":"W2036347736","doi":"10.1139/x08-122","title":"Area-based lidar-assisted estimation of forest standing volume","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Center for Innovative Medicine; Ministero dell'Università e della Ricerca","keywords":"Lidar; Estimator; Environmental science; Volume (thermodynamics); Canopy; Remote sensing; Sampling (signal processing); Confidence interval; Forest inventory; Sampling design; Statistics; Tree canopy; Sample (material); Mathematics; Geography; Forest management; Computer science; Agroforestry; Population; Filter (signal processing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000668695,0.0002395868,0.0004965817,0.001303272,0.00009828733,0.0003023669,0.0004769844,0.0002588044,0.0003982947],"category_scores_gemma":[0.001499973,0.0002147448,0.000223788,0.0005102037,0.0001298742,0.0002878903,0.0003201746,0.0001187671,0.0002380432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001734955,"about_ca_system_score_gemma":0.0001743915,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007234135,"about_ca_topic_score_gemma":0.001689088,"domain_scores_codex":[0.9994441,0.0002641733,0.0000193981,0.00009760525,0.0001524194,0.00002231678],"domain_scores_gemma":[0.9991996,0.0003346051,0.0001433732,0.00007146263,0.0002229995,0.00002800626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007254912,0.0001519525,0.0816998,0.0003096803,0.0002197897,0.0001286339,0.0001673481,0.1779,0.2016448,0.0008339959,0.0006568509,0.5355617],"study_design_scores_gemma":[0.000057331,0.0002795853,0.07485414,0.00001708784,0.00006232652,0.0002426405,0.00002784794,0.9002882,0.02252224,0.0006746489,0.0009216901,0.00005224374],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5900167,0.0004594995,0.4070356,0.00002277043,0.00001412816,0.00004978588,0.000358215,0.0008285205,0.001214758],"genre_scores_gemma":[0.8714445,0.00007762607,0.1276714,0.00001120101,0.00001594243,0.00005581446,0.0003097377,0.00002694384,0.0003868244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9992766,"threshold_uncertainty_score":0.003536403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06079937270841095,"score_gpt":0.3031568131804322,"score_spread":0.2423574404720212,"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."}}