{"id":"W4328026509","doi":"10.2139/ssrn.4394258","title":"The Challenge of Spatialising Point-Source Water Quality Monitoring Data for Multi-Substance Risk Assessment in the Canadian Oil Sands Region","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Tributary; Risk assessment; Environmental monitoring; Water quality; Environmental resource management; Water resource management; Hydrology (agriculture); Environmental protection; Geography; Environmental engineering; Ecology; Cartography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002961771,0.0004296238,0.0004065082,0.002088334,0.001342751,0.002830405,0.001158928,0.000772005,0.0007213088],"category_scores_gemma":[0.01350897,0.0002313769,0.000393272,0.005330354,0.001236641,0.001464601,0.001748799,0.0008236596,0.0001885247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00667807,"about_ca_system_score_gemma":0.02686225,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9590589,"about_ca_topic_score_gemma":0.970175,"domain_scores_codex":[0.9984766,0.0003487768,0.000106388,0.000286485,0.0005791049,0.0002027015],"domain_scores_gemma":[0.9933529,0.001981369,0.0006636655,0.0005726548,0.003232186,0.0001972667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001047283,0.00006773308,0.2223182,0.0006792222,0.0003227115,0.0002203101,0.002112009,0.2281035,0.01032616,0.01573711,0.01189268,0.5081157],"study_design_scores_gemma":[0.00005626288,0.00006428401,0.6731731,0.0007348227,0.0002793768,0.0003530863,0.0149307,0.171816,0.008558912,0.03671947,0.09307063,0.0002433582],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8129988,0.008857344,0.08557646,0.03648477,0.0002552917,0.000270632,0.01762188,0.0006505385,0.03728437],"genre_scores_gemma":[0.9283859,0.003763511,0.06286144,0.0007104863,0.00007275463,0.00006099393,0.001734336,0.00008949194,0.002321164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04094112,"threshold_uncertainty_score":0.08236438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04248524889982783,"score_gpt":0.3008575873393607,"score_spread":0.2583723384395328,"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."}}