{"id":"W2399538295","doi":"10.1016/j.agee.2016.04.023","title":"Tradeoffs between forage quality and soil fertility: Lessons from Himalayan rangelands","year":2016,"lang":"en","type":"article","venue":"Agriculture Ecosystems & Environment","topic":"Rangeland Management and Livestock Ecology","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; Peking University; Lanzhou University; National Science Foundation","keywords":"Agronomy; Rangeland; Grazing; Biology; Biomass (ecology); Specific leaf area; Nutrient; Soil fertility; Forage; Soil water; Botany; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004551594,0.0003275579,0.0004465395,0.00002491331,0.0002143108,0.00003803517,0.0002851301,0.0002105977,0.002045478],"category_scores_gemma":[0.00001499359,0.0001838346,0.0001106091,0.00006951414,0.0001308989,0.000264752,0.0003164822,0.0001269768,0.0008733529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002462023,"about_ca_system_score_gemma":0.000002623517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008695159,"about_ca_topic_score_gemma":0.001672002,"domain_scores_codex":[0.9976923,0.0002382713,0.0004642861,0.0007700737,0.0003579062,0.00047713],"domain_scores_gemma":[0.9989436,0.0001868168,0.0002032386,0.0004194687,0.000002061219,0.0002448164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003051515,0.000129425,0.903266,0.00001843206,0.0001251519,0.00001105256,0.0002897448,0.000008451452,0.07725994,0.0001240906,0.01098545,0.007751779],"study_design_scores_gemma":[0.0009350211,0.00008609607,0.87714,0.0000209163,0.00007616552,0.000002032288,0.0001013685,0.000006707667,0.002061178,0.0004249597,0.1188241,0.0003214237],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910297,0.0001890652,0.000849032,0.003907151,0.000153479,0.0005760689,0.0001785892,0.00007308274,0.003043874],"genre_scores_gemma":[0.9964473,0.0001574187,0.000114149,0.00008048204,0.0002073092,0.0001213293,0.00007795481,0.00001874974,0.002775332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1078387,"threshold_uncertainty_score":0.9999046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788027441998458,"score_gpt":0.2245539066129273,"score_spread":0.2066736321929428,"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."}}