{"id":"W2966490943","doi":"10.3390/f10080658","title":"Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship","year":2019,"lang":"en","type":"article","venue":"Forests","topic":"Forest ecology and management","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; University of Ottawa","funders":"State Key Laboratory of Remote Sensing Science","keywords":"Biomass (ecology); Statistics; Tree allometry; Mathematics; Volume (thermodynamics); Allometry; Parametric statistics; Regression analysis; Regression; Seemingly unrelated regressions; Field (mathematics); Forest inventory; Econometrics; Environmental science; Ecology; Forest management; Biomass partitioning; Agroforestry; 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":[0.01575091,0.003399401,0.002284927,0.00252532,0.0009994974,0.001670163,0.002133051,0.001205245,0.004551242],"category_scores_gemma":[0.05768916,0.001073928,0.003073541,0.002022175,0.0008126055,0.001996923,0.002814182,0.00488839,0.001487556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006100516,"about_ca_system_score_gemma":0.001860119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004829997,"about_ca_topic_score_gemma":0.004126098,"domain_scores_codex":[0.9901641,0.005635272,0.000969341,0.001577782,0.001340663,0.0003128416],"domain_scores_gemma":[0.9620365,0.03100119,0.001906787,0.002806272,0.00206751,0.0001817596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006333347,0.0005975917,0.02257407,0.001223929,0.001197753,0.000744552,0.001608464,0.4175334,0.042859,0.03246468,0.005834778,0.4727285],"study_design_scores_gemma":[0.00004061728,0.0001815844,0.00465412,0.00006517339,0.0001327526,0.0001238585,0.0001138399,0.9683148,0.0127418,0.007621543,0.005930979,0.00007902279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01626956,0.00010247,0.9802223,0.00005439797,0.00003000597,0.0001740738,0.0002364658,0.002493384,0.0004173262],"genre_scores_gemma":[0.1730621,0.0001715007,0.8212841,0.00009386166,0.00004227983,0.0008108666,0.001424572,0.002059356,0.001051373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01575091,"threshold_uncertainty_score":0.08329982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02197023604549277,"score_gpt":0.2841589002108071,"score_spread":0.2621886641653143,"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."}}