{"id":"W2534100142","doi":"10.1007/s11356-016-7890-8","title":"A Bayesian approach of high impaired river reaches identification and total nitrogen load estimation in a sparsely monitored basin","year":2016,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Tianjin University","keywords":"Watershed; Environmental science; Drainage basin; Hydrology (agriculture); Structural basin; Sparrow; Population; Bayesian probability; Statistics; Ecology; Geography; Mathematics; Cartography; Biology; Geology; Computer science","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":[0.001593912,0.000518619,0.001086763,0.001116638,0.0006021349,0.001047723,0.001463791,0.001488972,0.0009170499],"category_scores_gemma":[0.005036725,0.0009635754,0.0007417871,0.0008168793,0.0007309926,0.000908381,0.001211839,0.0007245389,0.0001609598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006515877,"about_ca_system_score_gemma":0.001525268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02458357,"about_ca_topic_score_gemma":0.02249878,"domain_scores_codex":[0.9995154,0.0001561863,0.00003476533,0.0001520886,0.00007848391,0.00006297062],"domain_scores_gemma":[0.9978185,0.001515014,0.0002070077,0.00007898,0.0002836075,0.00009693569],"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":[0.0001236649,0.00008553339,0.00613245,0.0000551483,0.0001329106,0.0001457509,0.0001121458,0.9333845,0.002332208,0.004880821,0.0004837989,0.05213106],"study_design_scores_gemma":[0.000003663521,0.000006706724,0.0006252955,0.000002648285,0.00000978806,0.000007480614,0.000006488612,0.9981177,0.0000939856,0.001074214,0.00004643032,0.000005624805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364416,0.000221956,0.8614634,0.0002371224,0.00001997221,0.00003704005,0.0001053212,0.0001968682,0.001276734],"genre_scores_gemma":[0.8871443,0.0002035383,0.1095235,0.00009419917,0.00006471886,0.00007950958,0.0002511243,0.00004139681,0.00259773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02458357,"threshold_uncertainty_score":0.04888099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680434340373627,"score_gpt":0.25717919266817,"score_spread":0.2303748492644337,"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."}}