{"id":"W2734413295","doi":"10.5558/tfc2017-017","title":"China’s Natural Forest Protection Program: Progress and impacts","year":2017,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Natural forest; Forest protection; Flooding (psychology); Environmental protection; Natural (archaeology); Geography; Government (linguistics); Forestry; Agroforestry; Forest management; Environmental science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001942846,0.0005066856,0.0002638383,0.001969387,0.0006342413,0.001581972,0.0005975551,0.0004864823,0.003512376],"category_scores_gemma":[0.001349342,0.0001307538,0.0002568068,0.00273577,0.0005596128,0.001319404,0.0009009311,0.000810058,0.0003530661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002682409,"about_ca_system_score_gemma":0.0115754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05309023,"about_ca_topic_score_gemma":0.06820665,"domain_scores_codex":[0.9993008,0.0000874297,0.00006485332,0.00008292271,0.0003306322,0.0001333978],"domain_scores_gemma":[0.9990575,0.0001482081,0.0001365801,0.0000251366,0.0004374457,0.000195142],"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":[0.00004468028,0.00006032336,0.01125204,0.006449473,0.00005775517,0.0002138869,0.0005090876,0.0009316315,0.0007761841,0.01103329,0.05028046,0.9183911],"study_design_scores_gemma":[0.00001694049,0.0001681722,0.05563785,0.003472205,0.00008403161,0.0002502398,0.000806104,0.0004067918,0.0008211341,0.001541034,0.936756,0.00003949254],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.0239974,0.8991238,0.0009681408,0.01791699,0.002025126,0.0001286292,0.001106145,0.0001306049,0.05460307],"genre_scores_gemma":[0.08262782,0.9062958,0.001082795,0.002196921,0.0008379103,0.00006573345,0.001094341,0.00002234658,0.005776423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05309023,"threshold_uncertainty_score":0.1055624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138146804760396,"score_gpt":0.2366909295357544,"score_spread":0.2228762490597148,"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."}}