{"id":"W4310047981","doi":"10.1139/cjfr-2022-0062","title":"Measurement uncertainty in a national forest inventory: results from the northern region of the USA","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Forest Service; U.S. Department of Agriculture","keywords":"Forest inventory; Environmental science; Basal area; Biomass (ecology); Quality assurance; Forest management; Forestry; Uncertainty analysis; Statistics; Geography; Environmental resource management; Agroforestry; Mathematics; Ecology; Service (business); Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00969157,0.0002253843,0.0003843252,0.00117222,0.0006601929,0.0009894355,0.0005763964,0.0003033972,0.0005469068],"category_scores_gemma":[0.02543151,0.0002217132,0.0004968657,0.003624672,0.0005872007,0.001009848,0.001084161,0.0004592729,0.0001724428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00183093,"about_ca_system_score_gemma":0.001765959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3165764,"about_ca_topic_score_gemma":0.4122837,"domain_scores_codex":[0.9948823,0.001746909,0.000446016,0.0007222056,0.001974837,0.0002277632],"domain_scores_gemma":[0.9774364,0.00940776,0.003523129,0.00217931,0.007006952,0.0004465833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000618959,0.00003741948,0.9840142,0.00002880115,0.0001553789,0.00003887883,0.0005716456,0.001840022,0.0001118446,0.000193964,0.001566094,0.01137975],"study_design_scores_gemma":[0.000005271843,0.00002543416,0.9946824,0.00002800455,0.00006597473,0.00004922306,0.000639492,0.002480804,0.0001595636,0.0001781631,0.001672777,0.00001292642],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891235,0.0004434702,0.001632022,0.0002171787,0.00001159058,0.00002692263,0.004397525,0.0000362132,0.00411161],"genre_scores_gemma":[0.9912355,0.0002059314,0.00160686,0.0001151313,0.00001091424,0.00004010369,0.006434455,0.00001457739,0.00033654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3165764,"threshold_uncertainty_score":0.6294674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08017167282765121,"score_gpt":0.2767870121525987,"score_spread":0.1966153393249475,"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."}}