{"id":"W3184487972","doi":"10.1093/forsci/fxab023","title":"Detection and Quantification of Coarse Woody Debris in Natural Forest Stands Using Airborne LiDAR","year":2021,"lang":"en","type":"article","venue":"Forest Science","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Forests; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coarse woody debris; Large woody debris; Environmental science; Lidar; Volume (thermodynamics); Snag; Biomass (ecology); Elevation (ballistics); Biodiversity; Forestry; Remote sensing; Habitat; Ecology; Geography; Mathematics; Biology","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":[0.0003000651,0.00006962059,0.0001127481,0.00003243164,0.0003568003,0.00002987374,0.000136206,0.00004934813,0.00001189015],"category_scores_gemma":[0.0001817729,0.00003261415,0.00002516143,0.000858322,0.0005774098,0.0002519799,0.0001303718,0.00007640338,0.000002695715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000035941,"about_ca_system_score_gemma":0.00002451333,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004484267,"about_ca_topic_score_gemma":0.0252197,"domain_scores_codex":[0.9992138,0.00002715152,0.0001337001,0.0002630377,0.000164122,0.0001981905],"domain_scores_gemma":[0.9996334,0.00007226891,0.00007166786,0.00004401456,0.0001399348,0.00003871302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003284049,0.00005972589,0.7453442,0.000007718672,0.000003652308,0.000008422991,0.0001358822,0.0001998491,0.2490405,0.001152363,0.000005969724,0.004008869],"study_design_scores_gemma":[0.0001017513,0.00009860793,0.9776099,0.00001665766,0.00000596184,0.00001059411,0.0005804625,0.004932201,0.01574278,0.0007449324,0.00007685834,0.00007931396],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992462,0.0002079604,0.00003417352,0.0002176422,0.0001588639,0.00007645879,0.00001092523,0.00001155791,0.00003619256],"genre_scores_gemma":[0.9997358,0.000039129,0.0001468885,0.00002838422,0.00001921591,0.000001440364,0.000005594429,2.490752e-7,0.00002328309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2332977,"threshold_uncertainty_score":0.9925675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709750834273933,"score_gpt":0.2332120901886416,"score_spread":0.2061145818459023,"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."}}