{"id":"W4387886437","doi":"10.2139/ssrn.4610767","title":"Economic Impacts of Climate Change on Forests: A Picus-Landis-Cge Modeling Approach","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University; Government of New Brunswick; University of Fredericton; University of New Brunswick","funders":"","keywords":"Computable general equilibrium; Climate change; Environmental science; Economics; Natural resource economics; Climatology; Macroeconomics; Geology; Oceanography","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":[0.0008729829,0.0008078942,0.001515695,0.0009164254,0.0009303036,0.00283662,0.00180689,0.002415179,0.004934975],"category_scores_gemma":[0.002782721,0.0009045204,0.001165004,0.00146751,0.0007657486,0.002027899,0.00132849,0.001734832,0.0005035234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001689603,"about_ca_system_score_gemma":0.001774106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04709186,"about_ca_topic_score_gemma":0.02586157,"domain_scores_codex":[0.9997099,0.0001360156,0.00001292807,0.00004414501,0.000055264,0.00004171518],"domain_scores_gemma":[0.9993129,0.0004115866,0.00005859599,0.00005585742,0.00007983956,0.00008126617],"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":[0.00001553058,0.00001998442,0.0004493915,0.0000110576,0.00003890864,0.00002944357,0.000008841802,0.9892237,0.00008512721,0.008323003,0.0005580062,0.00123705],"study_design_scores_gemma":[0.000008579876,0.000002845989,0.0001779994,0.000002337689,0.000008243281,0.000003832177,0.00000555098,0.9961649,0.00004286462,0.00324994,0.0003289289,0.000003984564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4851914,0.002130281,0.3432649,0.009893403,0.000684262,0.0001912486,0.006032309,0.001598984,0.1510132],"genre_scores_gemma":[0.9614915,0.000908811,0.02370294,0.0003762759,0.0002995901,0.0001784832,0.0009488384,0.0004453667,0.01164827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04709186,"threshold_uncertainty_score":0.0936355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02952337983644229,"score_gpt":0.2681317290006562,"score_spread":0.2386083491642139,"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."}}