{"id":"W2030364282","doi":"10.1007/s00468-010-0452-7","title":"Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand","year":2010,"lang":"en","type":"article","venue":"Trees","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":178,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Canopy; Environmental science; Remote sensing; Laser scanning; Vegetation (pathology); Biomass (ecology); Tree canopy; Understory; Sampling (signal processing); Laser; Filter (signal processing); Ecology; Geography; Computer science; Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001169664,0.00009592861,0.0001366628,0.00005588178,0.00009345267,0.00005431573,0.0001049168,0.00005901272,0.00004452849],"category_scores_gemma":[0.00005588321,0.00008708242,0.00001746689,0.0002738475,0.0001433509,0.0000813638,0.00007765885,0.0001744603,0.0000263622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004318434,"about_ca_system_score_gemma":0.00001019892,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01504044,"about_ca_topic_score_gemma":0.06396694,"domain_scores_codex":[0.9993124,0.00002379671,0.000145012,0.0002246611,0.000123511,0.0001705911],"domain_scores_gemma":[0.9995806,0.0001077829,0.00004824951,0.0001823211,0.000002221326,0.00007881373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007721181,0.00005867891,0.8942519,0.000001395222,0.00000971859,0.00000965973,0.0007307989,0.001007332,0.0841388,0.00001179688,0.0004100983,0.01929258],"study_design_scores_gemma":[0.0009530377,0.00001678756,0.9664108,0.0000124759,0.00001080527,0.000001990343,0.0001050147,0.02284897,0.006150723,0.0001667219,0.0032031,0.0001196332],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971861,0.00002728571,0.0001543265,0.0001210843,0.0001190177,0.0001202248,0.000004344056,0.00003168188,0.002235981],"genre_scores_gemma":[0.9969374,0.000007042284,0.002892923,0.00001667445,0.00006248094,0.000002213656,0.00001122015,0.000009777153,0.00006024499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07798807,"threshold_uncertainty_score":0.9915185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01604481376259611,"score_gpt":0.2389886413465206,"score_spread":0.2229438275839245,"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."}}