{"id":"W1515107010","doi":"","title":"Tributary Loadings of Priority Pollutants to Lake Ontario: A Prototype Approach Employing Surrogate Parameters","year":2011,"lang":"en","type":"article","venue":"SUNY Digital Repository Support (State University of New York System)","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tributary; Environmental science; Pollutant; Hydrology (agriculture); Watershed; Water resource management; Environmental engineering; Computer science; Geography; Engineering; Cartography; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001565634,0.0002514055,0.0004589118,0.0001097925,0.0001360439,0.00003435307,0.000522925,0.0001017613,0.00004524803],"category_scores_gemma":[0.00001162888,0.0002671753,0.0001996997,0.0003438957,0.0002235149,0.0006057951,0.0003078674,0.000137721,0.00006311278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004491834,"about_ca_system_score_gemma":0.0001511763,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03514665,"about_ca_topic_score_gemma":0.003565479,"domain_scores_codex":[0.9980921,0.00004514821,0.0004370256,0.00051325,0.0004903392,0.0004221306],"domain_scores_gemma":[0.9986942,0.00002072468,0.0004297328,0.0004205864,0.00003616255,0.0003986118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007158482,0.0001841729,0.9859045,0.0001146256,0.00007857615,0.0001615445,0.01101539,0.0001188196,0.0001280764,0.000005882308,0.0005187287,0.001053823],"study_design_scores_gemma":[0.003272427,0.002827291,0.9612867,0.0005194227,0.0002638223,0.0003653234,0.01179854,0.0003153477,0.004344753,0.0003848429,0.01310437,0.001517137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674141,0.000002943446,0.0004177142,0.000004747201,0.0002392977,0.0008203582,0.000153745,0.00008455928,0.0308625],"genre_scores_gemma":[0.9888877,6.036636e-7,0.00161564,0.000007664135,0.000007098611,0.000001092246,0.00002154952,0.00001916319,0.009439546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03158117,"threshold_uncertainty_score":0.9999781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061940714667134,"score_gpt":0.173275662832509,"score_spread":0.1526562556858377,"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."}}