{"id":"W2898821140","doi":"10.1016/j.ecolmodel.2018.10.011","title":"Estimating model- and sampling-related uncertainty in large-area growth predictions","year":2018,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Forest ecology and management","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Variance (accounting); Sampling (signal processing); Estimator; Statistics; Variance decomposition of forecast errors; Econometrics; Term (time); Mathematics; Variance reduction; Environmental science; Computer science; Monte Carlo method; Economics","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":[0.01344411,0.0006092309,0.0006504544,0.0007101955,0.0003938297,0.001499079,0.001253423,0.001347825,0.000577609],"category_scores_gemma":[0.06581322,0.0008275431,0.0007380247,0.0006658251,0.0008772668,0.001968182,0.001150962,0.0009936417,0.0001264916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154634,"about_ca_system_score_gemma":0.0007694641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01244749,"about_ca_topic_score_gemma":0.01341996,"domain_scores_codex":[0.997835,0.001201932,0.0001264703,0.0004505306,0.0002403139,0.0001458583],"domain_scores_gemma":[0.9092536,0.08326206,0.002774341,0.002767695,0.001553455,0.0003888715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001015569,0.00004073307,0.03017787,0.00002541869,0.0001079556,0.00003652695,0.00004113837,0.9598633,0.0003486858,0.001649371,0.0001734491,0.007434059],"study_design_scores_gemma":[0.000006322447,0.000009681246,0.005318505,0.000005647183,0.00001416807,0.00001440817,0.00001241956,0.9916454,0.0003793252,0.002525438,0.00006066611,0.000008108123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8402883,0.0003871719,0.1567067,0.000554219,0.00003470323,0.00003117989,0.000547887,0.0002725038,0.001177439],"genre_scores_gemma":[0.9926898,0.00004256397,0.006721173,0.0000366993,0.00001445379,0.00001308035,0.0003030234,0.00002206981,0.0001572117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01344411,"threshold_uncertainty_score":0.07110012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709549652500651,"score_gpt":0.2429807590463359,"score_spread":0.2158852625213294,"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."}}