{"id":"W2560413850","doi":"10.3390/f8040106","title":"Biophysical and Economic Analysis of Black Spruce Regeneration in Eastern Canada Using Global Climate Model Productivity Outputs","year":2017,"lang":"en","type":"article","venue":"Forests","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; McMaster University","funders":"","keywords":"Productivity; Climate change; Stumpage; Black spruce; Environmental science; Net present value; Carbon sequestration; Greenhouse gas; Primary production; Natural resource economics; Regeneration (biology); Present value; Taiga; Ecosystem; Economics; Geography; Agricultural economics; Ecology; Forestry; Production (economics); Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000366531,0.0004406953,0.0002134723,0.0007056718,0.0007931537,0.000939528,0.0006102243,0.000273628,0.00102575],"category_scores_gemma":[0.0006734176,0.0001610744,0.0006257754,0.001267409,0.0003967087,0.0002738214,0.0003295455,0.0002646375,0.00007518056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01985609,"about_ca_system_score_gemma":0.008630593,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9827229,"about_ca_topic_score_gemma":0.9875364,"domain_scores_codex":[0.9998358,0.00001908451,0.000005430662,0.00003014266,0.00004190705,0.0000674114],"domain_scores_gemma":[0.9996904,0.00006485898,0.00003121233,0.00001897501,0.0001364882,0.00005805301],"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":[0.0003439999,0.0001495936,0.445914,0.00009467875,0.0002536767,0.0004450326,0.0003613245,0.5294456,0.003118917,0.003017573,0.00185519,0.01500055],"study_design_scores_gemma":[0.00005367103,0.00007298327,0.6508152,0.0000343374,0.0001165525,0.00008071418,0.001266593,0.3425831,0.001158055,0.000677564,0.003070436,0.00007064848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964149,0.00007452659,0.0001951745,0.00005115162,0.000001648493,0.00001093227,0.001283243,0.00001792267,0.001950524],"genre_scores_gemma":[0.9977394,0.0001034845,0.0003752489,0.00001322058,9.741285e-7,0.000005881041,0.001032338,0.000006171384,0.000723245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01985609,"threshold_uncertainty_score":0.1440668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036521813391461,"score_gpt":0.264454875385105,"score_spread":0.2440896572511904,"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."}}