{"id":"W2977150853","doi":"10.5539/jsd.v12n5p138","title":"Calculation of SS, TN and TP Specific Concentration Factors for Land-Use Types Using a Simple Watershed Model","year":2019,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Hydrology (agriculture); Environmental science; Land use; Limit (mathematics); Phosphorus; Paddy field; Detection limit; Mathematics; Statistics; Chemistry; Ecology; Biology; Geology","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":[0.0005484905,0.0008722379,0.0007929531,0.001234912,0.0003780676,0.0007139944,0.001071535,0.0004575651,0.002111453],"category_scores_gemma":[0.0011255,0.0004982052,0.00135816,0.00211395,0.0002156692,0.0009058214,0.0004022501,0.0003729924,0.000519368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204273,"about_ca_system_score_gemma":0.001574601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04455232,"about_ca_topic_score_gemma":0.03239884,"domain_scores_codex":[0.9997657,0.00003704297,0.00003124169,0.00009401211,0.00004602683,0.00002608288],"domain_scores_gemma":[0.9997296,0.00008001687,0.00003890636,0.00001615925,0.0001148021,0.00002062802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007280488,0.0001336791,0.04417143,0.0001121524,0.0002088121,0.0002056022,0.0001015826,0.9104033,0.006898937,0.002897383,0.001316358,0.03347797],"study_design_scores_gemma":[0.00001093045,0.00001043035,0.003993971,0.000002410144,0.00002659955,0.00002197381,0.00001372026,0.9942698,0.0004285268,0.0005023286,0.0007091346,0.00001003362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5425647,0.0002078988,0.4460287,0.0001380452,0.00006117025,0.0003738615,0.003929036,0.002115744,0.004580737],"genre_scores_gemma":[0.8502879,0.0002715948,0.1385977,0.0000443629,0.00002559267,0.0006756776,0.004700348,0.0002132474,0.005183683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04455232,"threshold_uncertainty_score":0.08858603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183024388877712,"score_gpt":0.2223905691487121,"score_spread":0.2040881302609409,"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."}}