{"id":"W2070610531","doi":"10.1007/s11676-012-0232-x","title":"Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests","year":2012,"lang":"en","type":"article","venue":"Journal of Forestry Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Canopy; Mean squared error; Mathematics; Biomass (ecology); Tree canopy; Taiga; Environmental science; Closure (psychology); Boreal; Tree (set theory); Statistics; Atmospheric sciences; Ecology; Forestry; Geography; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112602,0.0007606282,0.0005494567,0.001736037,0.001033289,0.0004473772,0.0008087766,0.0003501768,0.0002725316],"category_scores_gemma":[0.001647887,0.0005336458,0.0005895099,0.001223678,0.0006629283,0.0008864073,0.0004428538,0.0001960622,0.00004590391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004756,"about_ca_system_score_gemma":0.001230708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1505663,"about_ca_topic_score_gemma":0.2384415,"domain_scores_codex":[0.9995958,0.00008321182,0.00004330786,0.0001301096,0.00007729994,0.00007030197],"domain_scores_gemma":[0.9989634,0.000505969,0.0001919384,0.00007347651,0.0001676429,0.00009755998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001475609,0.0001109842,0.9727421,0.00004592709,0.0001276313,0.0001371252,0.0004960832,0.007053572,0.002188915,0.0001062672,0.000123819,0.01671991],"study_design_scores_gemma":[0.00001069801,0.00004077929,0.960674,0.000003735552,0.00007712586,0.000077799,0.0003915198,0.03815361,0.0003736072,0.00009690758,0.00007737376,0.0000227989],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999262,0.00004558539,0.0005600716,0.000005960446,0.00000140305,0.00000308884,0.00004660082,0.000007759082,0.00006747945],"genre_scores_gemma":[0.9989017,0.00002258897,0.000851501,0.000003226785,0.000002248229,0.000006621704,0.0001402689,0.000001919081,0.0000697814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1505663,"threshold_uncertainty_score":0.2993797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0640209668204377,"score_gpt":0.4217582728671322,"score_spread":0.3577373060466945,"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."}}