{"id":"W2111526473","doi":"10.1016/j.ecolmodel.2013.03.016","title":"Incorporating weather sensitivity in inventory-based estimates of boreal forest productivity: A meta-analysis of process model results","year":2013,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Canadian Forest Service; University of Toronto; McMaster University; University of Alberta","funders":"","keywords":"Environmental science; Eddy covariance; Primary production; Taiga; Boreal; Atmospheric sciences; Climate change; Sensitivity (control systems); Forest inventory; Climatology; Ecosystem; Ecology; Forest management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006624536,0.0001333738,0.000563211,0.0001147059,0.00003512809,0.00001116117,0.0001009784,0.00009484428,0.00004055548],"category_scores_gemma":[0.00006280405,0.00009606362,0.000209407,0.0005615103,0.0001360243,0.0001828116,0.00006251808,0.0001256849,0.000003329127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005060557,"about_ca_system_score_gemma":0.00001462529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001045326,"about_ca_topic_score_gemma":0.001964404,"domain_scores_codex":[0.9987155,0.00007527267,0.0004570775,0.0003372381,0.0002316256,0.0001833413],"domain_scores_gemma":[0.9993123,0.0001314239,0.0002857281,0.0001945054,0.00002963578,0.00004636197],"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":[0.00001576548,0.0002055457,0.1173011,0.000007930556,0.0004790384,0.000002020213,0.0001122559,0.8814594,0.0002926401,0.00009626004,7.731839e-7,0.00002722653],"study_design_scores_gemma":[0.0001152072,0.00003374875,0.01698223,0.000002670356,0.002286304,3.075925e-7,0.000008810155,0.9691485,0.0002693428,0.01105203,1.242183e-7,0.0001007806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9240343,0.000006514668,0.07439719,0.00008742537,0.000004232695,0.0002648233,0.00003122216,0.00001512915,0.001159167],"genre_scores_gemma":[0.9779426,6.850717e-7,0.02192525,0.00001647738,0.000001779388,0.00004582601,0.00003772381,0.000006113513,0.00002358945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1003189,"threshold_uncertainty_score":0.3917361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05218389248579106,"score_gpt":0.2479097770383881,"score_spread":0.195725884552597,"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."}}