{"id":"W2747389116","doi":"10.1007/s11356-017-9933-1","title":"Assessing landscape and contaminant point-sources as spatial determinants of water quality in the Vermilion River System, Ontario, Canada","year":2017,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Greo; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Trillium Foundation","keywords":"Water quality; Tributary; Watershed; Environmental science; Land cover; Hydrology (agriculture); Surface runoff; Land use; Water resource management; Geography; Ecology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007938158,0.0002399997,0.0003869068,0.001449073,0.002433123,0.001739649,0.001234033,0.0003982829,0.001043254],"category_scores_gemma":[0.002770848,0.0003247958,0.0004609379,0.003615725,0.001218997,0.0004768512,0.00120155,0.0003676624,0.0001200766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02840158,"about_ca_system_score_gemma":0.02694816,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.997162,"about_ca_topic_score_gemma":0.9992538,"domain_scores_codex":[0.9990952,0.0001814991,0.00006154717,0.0001648729,0.0002891063,0.0002078642],"domain_scores_gemma":[0.9977353,0.0003692974,0.0003537534,0.00008169022,0.001172559,0.0002875567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008795466,0.0000352145,0.9863174,0.00005545622,0.0001245626,0.0001028052,0.001760678,0.001468443,0.0006050615,0.000332678,0.0009763721,0.0081333],"study_design_scores_gemma":[0.000006229211,0.00001293666,0.9956327,0.000021286,0.00003495372,0.00001799747,0.002232345,0.001141925,0.00009794407,0.00005402613,0.000739425,0.000008223845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962017,0.0003315941,0.0003009895,0.0001888249,0.000004474034,0.00004249766,0.001137267,0.0000116563,0.001780942],"genre_scores_gemma":[0.9971451,0.0002125352,0.0005311408,0.00002355371,0.000001906037,0.00001643907,0.0005648787,0.000004859587,0.001499659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02840158,"threshold_uncertainty_score":0.2060689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04878115822442232,"score_gpt":0.3494468355672222,"score_spread":0.3006656773427999,"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."}}