{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003530688,0.0002046827,0.0002136462,0.000935709,0.0001992261,0.0004202736,0.0002304083,0.0002405254,0.0003184817],"category_scores_gemma":[0.0005738884,0.0001770808,0.000147581,0.0004601521,0.0001522872,0.0004855347,0.0003063704,0.0001635394,0.0001054466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001577235,"about_ca_system_score_gemma":0.0002816015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003300834,"about_ca_topic_score_gemma":0.005883598,"domain_scores_codex":[0.9998512,0.00002255395,0.000007109848,0.00003915473,0.00005409632,0.00002578923],"domain_scores_gemma":[0.9995716,0.000187852,0.00006150515,0.00003317714,0.0001132853,0.00003258779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005663878,0.000484826,0.3923814,0.0002420523,0.000167399,0.0002484114,0.0006263115,0.02202584,0.2796906,0.0009380949,0.0008946234,0.3017339],"study_design_scores_gemma":[0.00005616187,0.0003202705,0.6542625,0.00004140339,0.000102254,0.0005903231,0.0005212612,0.3104132,0.03109589,0.001220758,0.001314094,0.00006188326],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872903,0.0001763758,0.0118902,0.00001764649,0.00001042309,0.00001393051,0.00008649727,0.00007298151,0.0004415613],"genre_scores_gemma":[0.9826189,0.0000808015,0.01693499,0.000009839028,0.000008216004,0.000009770415,0.0001272384,0.000005633066,0.0002046711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003300834,"threshold_uncertainty_score":0.006563246,"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."}}