{"id":"W945952497","doi":"10.1079/9780851990026.0169","title":"How will new large-scale nature reserves in temperate and boreal forests affect the global structural wood products sector?","year":2005,"lang":"en","type":"book-chapter","venue":"CABI Publishing eBooks","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Natural resource economics; Temperate climate; Production (economics); Business; Population; Taiga; Temperate rainforest; Wood production; Scale (ratio); Agricultural economics; Economics; Agroforestry; Geography; Environmental science; Ecology; Forestry; Forest management; Ecosystem; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002573747,0.0001843434,0.00009992583,0.0002546971,0.000664327,0.002534918,0.0002664571,0.0007374844,0.006298115],"category_scores_gemma":[0.0002718657,0.0001271776,0.0001894891,0.0005262598,0.001119118,0.00276466,0.0004865011,0.0008341514,0.0009829613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545644,"about_ca_system_score_gemma":0.00113727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00982336,"about_ca_topic_score_gemma":0.0319693,"domain_scores_codex":[0.9999156,0.00001314807,0.000001978417,0.00001532718,0.00002378319,0.00003008137],"domain_scores_gemma":[0.9999421,0.00001886491,0.000009044294,0.000004782851,0.00001158177,0.00001371654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006043026,0.0001135155,0.009212274,0.0002931642,0.00001846553,0.0005653097,0.002763199,0.004309253,0.00222385,0.6386167,0.07488355,0.2669402],"study_design_scores_gemma":[0.00001249598,0.000080505,0.03194808,0.0003179258,0.00001714641,0.0005951942,0.003076372,0.001465812,0.0007722325,0.1144503,0.8472387,0.00002529106],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07862864,0.03264845,0.00150885,0.02413859,0.001181646,0.00001975833,0.0001666506,0.00005179549,0.8616557],"genre_scores_gemma":[0.6672782,0.04505758,0.00238003,0.005430935,0.0007265485,0.00003301181,0.000272718,0.00005198671,0.278769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00982336,"threshold_uncertainty_score":0.02106935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096480744281414,"score_gpt":0.2228137125692903,"score_spread":0.2118489051264762,"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."}}