{"id":"W2938960083","doi":"10.1371/journal.pone.0215238","title":"A critique of general allometry-inspired models for estimating forest carbon density from airborne LiDAR","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Forest Research Institute; Ministry of Natural Resources and Forestry; University of Regina","funders":"","keywords":"Basal area; Allometry; Tree allometry; Canopy; Lidar; Biomass (ecology); Environmental science; Weibull distribution; Atmospheric sciences; Temperate rainforest; Ecology; Physical geography; Forestry; Ecosystem; Mathematics; Geography; Biology; Remote sensing; Statistics; Physics; Biomass partitioning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.007607262,0.001627778,0.001690497,0.001436323,0.000647174,0.001816838,0.007090671,0.002365723,0.001860073],"category_scores_gemma":[0.02138224,0.001187743,0.002107275,0.002033867,0.002744114,0.003795193,0.001643081,0.004509035,0.001685192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002532391,"about_ca_system_score_gemma":0.001646248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01801948,"about_ca_topic_score_gemma":0.009960135,"domain_scores_codex":[0.9974604,0.001035552,0.0001195111,0.0005399042,0.0007670469,0.00007758359],"domain_scores_gemma":[0.9922472,0.005390866,0.0004656469,0.000897801,0.0008765585,0.0001217418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008376516,0.00006666161,0.01007341,0.0006485287,0.0004830566,0.0002255438,0.0004062639,0.5229508,0.001070786,0.356318,0.01408008,0.09359314],"study_design_scores_gemma":[0.00003459419,0.00004022345,0.003410349,0.0001743992,0.00005090416,0.0002877,0.00009646546,0.6281213,0.0003470568,0.3493706,0.01797959,0.00008694432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01380659,0.004933479,0.9592628,0.01128872,0.0005183545,0.00008047959,0.0009184442,0.0009300565,0.008260876],"genre_scores_gemma":[0.5293162,0.01207434,0.4295361,0.009234228,0.002145646,0.0009392711,0.001693686,0.001287981,0.01377246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01801948,"threshold_uncertainty_score":0.04023153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572379316781305,"score_gpt":0.2311029356875406,"score_spread":0.2053791425197276,"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."}}