{"id":"W2965094494","doi":"10.1016/j.rama.2019.06.001","title":"Grassland Degradation on the Qinghai-Tibetan Plateau: Reevaluation of Causative Factors","year":2019,"lang":"en","type":"article","venue":"Rangeland Ecology & Management","topic":"Rangeland Management and Livestock Ecology","field":"Environmental Science","cited_by":168,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Liga Portuguesa Contra o Cancro; Natural Science Foundation of Gansu Province; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Grassland degradation; Overgrazing; Grassland; Agroforestry; Livestock; Rangeland; Plateau (mathematics); Geography; Land degradation; Pastoralism; Sustainability; Environmental science; Grazing; Agriculture; Ecology; Biology; Forestry","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":[0.003148416,0.0003403013,0.0003685187,0.002219124,0.0008980596,0.001364847,0.0005035726,0.0004965873,0.0008098762],"category_scores_gemma":[0.002610807,0.0001520178,0.0007845643,0.001964651,0.001103968,0.0007905769,0.0007056083,0.0006581026,0.00003228567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00191172,"about_ca_system_score_gemma":0.002474799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09914788,"about_ca_topic_score_gemma":0.128106,"domain_scores_codex":[0.9994537,0.0001459203,0.00006107533,0.00009488897,0.0001253302,0.0001190031],"domain_scores_gemma":[0.9973938,0.0008014998,0.0008068459,0.0001216112,0.0006266738,0.0002495174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006146511,0.00001726881,0.9763766,0.0002325834,0.0001707408,0.0007994383,0.001535278,0.0002746871,0.000431204,0.0005669921,0.0001853636,0.01934839],"study_design_scores_gemma":[0.000002210472,0.00003309641,0.9965112,0.00008039005,0.00007964405,0.0001720014,0.001657446,0.000372016,0.00005234863,0.0002879213,0.0007456984,0.000006164509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800311,0.01532245,0.0004660495,0.002082507,0.00005362419,0.00002620202,0.0001673776,0.000009022559,0.001841609],"genre_scores_gemma":[0.996884,0.002471139,0.0002022199,0.0001771591,0.00005605632,0.000005142278,0.00009889609,0.000001061994,0.0001041933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09914788,"threshold_uncertainty_score":0.1971416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0121896171602656,"score_gpt":0.2132602275937739,"score_spread":0.2010706104335084,"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."}}