{"id":"W2416279773","doi":"","title":"Сравнительная характеристика лесов и ведения лесного хозяйства в разных странах","year":2015,"lang":"ru","type":"article","venue":"Лесохозяйственная информация","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geography; Sustainable forest management; Tropical and subtropical moist broadleaf forests; Forest management; Forest ecology; Forestry; China; Subtropics; Agroforestry; Environmental resource management; Environmental protection; Ecosystem; Environmental science; Ecology","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":[0.001406735,0.0002662544,0.0003007927,0.003204376,0.001227397,0.002348015,0.0003483984,0.0004159778,0.01085125],"category_scores_gemma":[0.002700674,0.0003524908,0.000627898,0.004346819,0.001206094,0.0008882043,0.0007847935,0.0008993273,0.003535339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009352984,"about_ca_system_score_gemma":0.0027691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004621577,"about_ca_topic_score_gemma":0.01014289,"domain_scores_codex":[0.9981319,0.000352663,0.0001455315,0.0003282006,0.0009097193,0.0001319664],"domain_scores_gemma":[0.9985406,0.0004962715,0.0002624048,0.0002343103,0.0003926624,0.0000737693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002232624,0.0001752435,0.02248427,0.0007175492,0.000122711,0.0007349963,0.003415026,0.003593379,0.04002843,0.1917143,0.009038131,0.7277527],"study_design_scores_gemma":[0.00003691726,0.0002624303,0.06248707,0.0003171452,0.0001944521,0.002298438,0.002546286,0.004416985,0.0338612,0.08811839,0.805276,0.0001847365],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2315936,0.02827151,0.2962198,0.002478768,0.001729479,0.000756047,0.005612073,0.000634638,0.4327041],"genre_scores_gemma":[0.7780018,0.01156424,0.1606618,0.0001416896,0.0003134516,0.0004937833,0.00165462,0.0002181302,0.0469505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01085125,"threshold_uncertainty_score":0.03630102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02899764160028371,"score_gpt":0.2523218680323679,"score_spread":0.2233242264320842,"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."}}