{"id":"W2165597803","doi":"10.1139/x09-025","title":"Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norges Forskningsråd","keywords":"Basal area; Standard deviation; Plot (graphics); Statistics; Mathematics; Canopy; Sampling (signal processing); Laser scanning; Monte Carlo method; Sample size determination; Sample (material); Remote sensing; Laser; Physics; Geography; Optics; Forestry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004040025,0.00007222634,0.0001348042,0.00009182372,0.0002903811,0.0001387198,0.0002814213,0.00004021584,0.00003472761],"category_scores_gemma":[0.0007108881,0.00005688213,0.00002212979,0.0002210678,0.0003966854,0.0002844174,0.00003781239,0.0002850742,0.000006779524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514679,"about_ca_system_score_gemma":0.0002534432,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02757795,"about_ca_topic_score_gemma":0.01682958,"domain_scores_codex":[0.9989123,0.0001254673,0.0001679433,0.0001699364,0.0003572647,0.0002670641],"domain_scores_gemma":[0.9987798,0.00043881,0.00006745909,0.0002778648,0.00005254571,0.0003835277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003245335,0.0004034767,0.1053609,0.0001011724,0.0001544686,0.0004587707,0.005442438,0.006400201,0.7243069,0.0002621843,0.01018764,0.1465973],"study_design_scores_gemma":[0.0003365654,0.0003497606,0.971508,0.0003827325,0.00001448027,0.00001388798,0.00029149,0.001336291,0.02371369,0.001438404,0.0005195909,0.00009511286],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978992,0.00009564038,0.0001424532,0.001340869,0.00002441104,0.0001056174,0.00002159047,0.000002460924,0.0003677111],"genre_scores_gemma":[0.9975979,0.00001070522,0.002232213,0.00005249252,0.00006605127,2.520604e-7,0.000005329951,0.0000081445,0.00002688349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8661471,"threshold_uncertainty_score":0.9788975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05727805873797558,"score_gpt":0.3146377601386789,"score_spread":0.2573597014007033,"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."}}