{"id":"W2212987338","doi":"10.1111/2041-210x.12505","title":"Correcting the overestimate of forest biomass carbon on the national scale","year":2015,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Forest ecology and management","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; McGill University; Université du Québec à Montréal","funders":"State Key Laboratory of Remote Sensing Science; Beijing Normal University; Chinese Academy of Sciences","keywords":"Biomass (ecology); Statistics; Scaling; Variance (accounting); Environmental science; Mathematics; Econometrics; Scale (ratio); Ecology; Geography; Economics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002848288,0.00005048561,0.00006976628,0.00002931898,0.0001016088,0.000002747662,0.00009596499,0.000063569,0.0000368428],"category_scores_gemma":[0.0005873629,0.00003050117,0.00001261945,0.0001297596,0.0003495217,0.00003401732,0.0001203929,0.00009941998,0.0000109492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001518253,"about_ca_system_score_gemma":0.00001000995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003165692,"about_ca_topic_score_gemma":0.003390846,"domain_scores_codex":[0.9991386,0.0004249534,0.0001163009,0.0001174995,0.00008075292,0.0001218834],"domain_scores_gemma":[0.9993114,0.0005269828,0.0000659873,0.00007154229,0.000007699774,0.00001641026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003638478,0.00004383672,0.9784244,0.000002435295,0.000005753253,4.566924e-7,0.0002328943,0.006890424,0.0001473431,0.01263055,0.0006812229,0.0009042763],"study_design_scores_gemma":[0.000144915,0.00008039327,0.884495,0.000002507128,0.00000539898,0.000006597056,0.000148368,0.02254963,0.0001920998,0.09217357,0.0001698295,0.00003171402],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812087,0.00001713336,0.003165256,0.0009593343,0.0003353358,0.0001593359,5.652115e-7,0.000006817864,0.01414748],"genre_scores_gemma":[0.9944677,0.000001425394,0.005152975,0.0001936527,0.00001319553,0.00003099623,5.68462e-7,0.000002327499,0.000137115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09392944,"threshold_uncertainty_score":0.1892172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0370918969367372,"score_gpt":0.3286420527173645,"score_spread":0.2915501557806273,"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."}}