{"id":"W2178097034","doi":"10.1139/cjfr-2015-0084","title":"LiDAR-supported estimation of change in forest biomass with time-invariant regression models","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Lidar; Canopy; Forest inventory; Thinning; Statistics; Mathematics; Standard error; Linear regression; Tree canopy; Regression analysis; Regression; Environmental science; Remote sensing; Geography; Forest management; Forestry; Agroforestry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00239629,0.0005027815,0.0006111513,0.0005718431,0.0001882521,0.000599757,0.001058386,0.0005294608,0.0003843741],"category_scores_gemma":[0.005040019,0.0004847181,0.0008854695,0.000541559,0.0003161918,0.0007701963,0.0005364333,0.0007015514,0.0001579529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000674997,"about_ca_system_score_gemma":0.0007009736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00996434,"about_ca_topic_score_gemma":0.01124554,"domain_scores_codex":[0.9991379,0.0004481593,0.00004858924,0.0001914695,0.0001079257,0.00006600969],"domain_scores_gemma":[0.9986425,0.0008284979,0.0001956819,0.0001340378,0.0001701545,0.00002908738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001247625,0.00006539503,0.009352009,0.00003841795,0.00008337472,0.00005696745,0.00006071317,0.9352661,0.004224603,0.002699463,0.00012871,0.04789949],"study_design_scores_gemma":[0.000004457587,0.00001513261,0.001386768,0.000001869069,0.000004798052,0.000007447674,0.000003621747,0.9978536,0.0003291329,0.0003290133,0.00005729512,0.000006809745],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2768653,0.0001193731,0.7219129,0.00005216197,0.00001505249,0.00003653786,0.0001899659,0.000383991,0.0004247257],"genre_scores_gemma":[0.8627071,0.00006367866,0.1360582,0.00001469512,0.00001279591,0.00009646327,0.0003131023,0.00003981293,0.0006941952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00996434,"threshold_uncertainty_score":0.0198127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09775653495865017,"score_gpt":0.3271446827271497,"score_spread":0.2293881477684995,"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."}}