{"id":"W2056687277","doi":"10.1139/x03-099","title":"Mapping aboveground tree biomass at the stand level from inventory information: test cases in Newfoundland and Quebec","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Forest Service; Natural Resources Canada","keywords":"Forest inventory; Biomass (ecology); Forestry; Tree (set theory); Plot (graphics); Environmental science; Statistics; Allometry; Mean squared error; Geography; Physical geography; Forest management; Mathematics; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.00204169,0.0007004768,0.0004269172,0.001644636,0.001323863,0.001585442,0.001776246,0.0007135805,0.001118302],"category_scores_gemma":[0.006635818,0.0003430187,0.0007105363,0.003882434,0.0007973831,0.0006594091,0.0009968295,0.0006010443,0.0001650031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02033035,"about_ca_system_score_gemma":0.007528057,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9867726,"about_ca_topic_score_gemma":0.9906142,"domain_scores_codex":[0.9984114,0.0004308478,0.0001067085,0.0003006502,0.0003225026,0.0004277868],"domain_scores_gemma":[0.9947692,0.001916471,0.0004857556,0.0005233332,0.002008874,0.0002963921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005048799,0.0005650403,0.8380406,0.0002541796,0.0004177797,0.002661049,0.001695606,0.1014258,0.002992517,0.001200264,0.003915893,0.04632639],"study_design_scores_gemma":[0.0001692183,0.0002255407,0.800686,0.00008723657,0.0002266022,0.0004827317,0.004084588,0.1868071,0.003304759,0.0002584103,0.003580232,0.00008762872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933568,0.0001454551,0.00145519,0.00009340019,0.000005058382,0.0001611826,0.002630423,0.0001002633,0.002052183],"genre_scores_gemma":[0.9898573,0.00009800955,0.005356473,0.00003704315,0.000002666977,0.0001428363,0.003341844,0.00002548202,0.001138345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02033035,"threshold_uncertainty_score":0.1475077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05315056350951537,"score_gpt":0.2715306645757364,"score_spread":0.218380101066221,"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."}}