{"id":"W2068189185","doi":"10.1016/j.jfe.2014.03.001","title":"The effect of collective forestland tenure reform in China: Does land parcelization reduce forest management intensity?","year":2014,"lang":"en","type":"article","venue":"Journal of Forest Economics","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; Beijing Forestry University; National Natural Science Foundation of China; Sveriges Lantbruksuniversitet; University of Missouri","keywords":"China; Forest management; Land tenure; Intensity (physics); Business; Natural resource economics; Economics; Agricultural economics; Environmental science; Agroforestry; Geography","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":[0.001436878,0.0002982896,0.0004883759,0.0005275609,0.0007256662,0.0008747506,0.000689218,0.0005147593,0.001743484],"category_scores_gemma":[0.002910819,0.000173382,0.0005880503,0.0009252403,0.001163421,0.0006974513,0.0009883017,0.0004947374,0.0000754771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004526661,"about_ca_system_score_gemma":0.00532143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1595618,"about_ca_topic_score_gemma":0.2598993,"domain_scores_codex":[0.9985611,0.000352779,0.0000577591,0.0001704617,0.0001917924,0.0006661458],"domain_scores_gemma":[0.9975135,0.0003324458,0.001023454,0.0001711012,0.0003671538,0.0005922905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003934749,0.0005260987,0.9700211,0.00006672575,0.0002683069,0.0003632098,0.0009285203,0.003458667,0.001613324,0.001566074,0.0007291744,0.0200652],"study_design_scores_gemma":[0.00002071718,0.0001226148,0.9972168,0.000006079754,0.00004466613,0.0000100815,0.0005822474,0.001132341,0.0002313694,0.0001386228,0.0004885849,0.000005808328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986688,0.0001568594,0.00005694417,0.0003794993,0.000004226707,0.00001295534,0.00004627418,0.000003718123,0.0006707424],"genre_scores_gemma":[0.9995714,0.00005422294,0.00003446351,0.00006413308,0.000003092618,0.000004531652,0.00004902318,6.2973e-7,0.0002184655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1595618,"threshold_uncertainty_score":0.317266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003177387466834395,"score_gpt":0.1985441989585889,"score_spread":0.1953668114917545,"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."}}