{"id":"W2782617180","doi":"10.1139/cjfr-2017-0221","title":"Multivariate estimation for accurate and logically consistent forest-attributes maps at macroscales","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; U.S. Forest Service; Canadian Forest Service; Arizona State University","keywords":"Taiga; Multivariate statistics; Forest inventory; Kriging; Consistency (knowledge bases); Estimation; Mathematics; Imputation (statistics); Statistics; Forest management; Econometrics; Computer science; Geography; Forestry; Missing data","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.005514201,0.0005469084,0.0006199246,0.001340253,0.0005557225,0.001707253,0.001088555,0.0003441381,0.001170305],"category_scores_gemma":[0.02365276,0.0005354826,0.0008426805,0.002022741,0.0006504727,0.001994099,0.001581772,0.001269219,0.0002449142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001490296,"about_ca_system_score_gemma":0.002531745,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08118771,"about_ca_topic_score_gemma":0.1493866,"domain_scores_codex":[0.9985599,0.0005819186,0.00009079789,0.0003454871,0.0002937966,0.000128152],"domain_scores_gemma":[0.9903589,0.004547349,0.00127746,0.001662497,0.002001451,0.0001523515],"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":[0.0001326794,0.00009150417,0.1874017,0.0002378486,0.0003826812,0.0001310464,0.000775711,0.6503413,0.006584711,0.02531299,0.002537323,0.1260706],"study_design_scores_gemma":[0.00001728963,0.00002223298,0.05876111,0.00004472119,0.00003382445,0.00004308657,0.0002320529,0.9144797,0.001650841,0.02247841,0.002182849,0.00005394806],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1511687,0.0001257867,0.8450916,0.0002172191,0.00001634505,0.00005103214,0.001513236,0.0009013745,0.0009145756],"genre_scores_gemma":[0.6612042,0.00008985739,0.3365266,0.00003642577,0.00001015714,0.00006302548,0.001713799,0.0001434802,0.0002124543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9188123,"threshold_uncertainty_score":0.1614303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0621693145297165,"score_gpt":0.3285073587760907,"score_spread":0.2663380442463741,"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."}}