{"id":"W2117546886","doi":"10.1139/cjfr-2012-0454","title":"Estimating stand-scale biomass, nutrient contents, and associated uncertainties for tree species of Canadian forests","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Nutrient; Biomass (ecology); Environmental science; Productivity; Phosphorus; Logging; Basal area; Bioenergy; Ecology; Agronomy; Biology; Biofuel; Chemistry","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":[0.0021203,0.0005094274,0.0003436918,0.002893532,0.001325597,0.001441026,0.001011682,0.0002907959,0.0005273449],"category_scores_gemma":[0.007224723,0.0003574351,0.0007341428,0.003864384,0.0004394286,0.0006649373,0.0006402654,0.0003113096,0.00009539354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02346825,"about_ca_system_score_gemma":0.01387177,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9784136,"about_ca_topic_score_gemma":0.9890981,"domain_scores_codex":[0.9990828,0.0000905339,0.00006547583,0.0002016952,0.0004356483,0.0001237467],"domain_scores_gemma":[0.9964006,0.001139825,0.000458084,0.0001856061,0.00171689,0.0000990124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006213327,0.00002030668,0.8953732,0.0000851657,0.0002250349,0.00007250654,0.0003699423,0.06570556,0.001276089,0.001069854,0.0006997942,0.03504049],"study_design_scores_gemma":[0.00001137226,0.00001627707,0.8503807,0.00004895327,0.0001220711,0.00007164911,0.0007190924,0.1413792,0.002416171,0.001350579,0.003419696,0.00006412021],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795499,0.0005394992,0.01049727,0.00008962685,0.00000388027,0.00004743673,0.007170436,0.0001161395,0.001985852],"genre_scores_gemma":[0.9742968,0.0002775468,0.01857873,0.00002353076,0.000002478346,0.000027117,0.006289834,0.00002551372,0.0004785121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02346825,"threshold_uncertainty_score":0.1702749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120431146290974,"score_gpt":0.2705005394100502,"score_spread":0.2292962279471405,"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."}}