{"id":"W2617459759","doi":"10.5539/jms.v7n2p65","title":"Quantifying the Environmental Benefits of Conserving Grassland","year":2017,"lang":"en","type":"article","venue":"Journal of Management and Sustainability","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Surface runoff; Grassland; Soil and Water Assessment Tool; Habitat; Sediment; Water quality; Resource (disambiguation); Hydrology (agriculture); Drainage basin; Water resource management; Agroforestry; Geography; Ecology; Streamflow","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":[0.000788581,0.0002604222,0.0001651231,0.0004475293,0.0002509342,0.0008022166,0.0002271391,0.0004123474,0.0007854986],"category_scores_gemma":[0.002047666,0.0001590107,0.0001852855,0.0009442555,0.0002676887,0.00063325,0.0003487962,0.0001830624,0.00005926984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688948,"about_ca_system_score_gemma":0.001325462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07052453,"about_ca_topic_score_gemma":0.1993308,"domain_scores_codex":[0.9996451,0.0001662877,0.00001375758,0.00005580197,0.00006697264,0.00005208443],"domain_scores_gemma":[0.9992611,0.0003501826,0.0001497537,0.00006139008,0.0001219913,0.00005558882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003130082,0.000344681,0.5024154,0.0001195985,0.0003686226,0.0001541203,0.0001037196,0.4480727,0.00800069,0.00218211,0.0004256431,0.03749978],"study_design_scores_gemma":[0.00004323897,0.0007608249,0.5299519,0.00003798858,0.000226725,0.00005515412,0.0008760073,0.4603091,0.002886364,0.002633226,0.002187128,0.00003224714],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980444,0.00003839197,0.0009368799,0.00004307987,0.000001123766,0.0000135884,0.0001992413,0.000009496563,0.0007138369],"genre_scores_gemma":[0.9983122,0.00004062158,0.00124576,0.00000871739,9.736816e-7,0.000006627661,0.0001737004,0.000001934629,0.0002094871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07052453,"threshold_uncertainty_score":0.140228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443642066005596,"score_gpt":0.2387475234027506,"score_spread":0.2243111027426946,"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."}}