{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007568175,0.0003945201,0.0005358058,0.0003598469,0.0007880349,0.001199936,0.00101845,0.0008334261,0.001829687],"category_scores_gemma":[0.00231072,0.0003278058,0.0004958085,0.0007509975,0.0003213728,0.0006302263,0.0005037381,0.0003695683,0.0001391374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001781998,"about_ca_system_score_gemma":0.003431063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2630145,"about_ca_topic_score_gemma":0.3874457,"domain_scores_codex":[0.9997917,0.00008331333,0.000008383126,0.00005099773,0.00003915426,0.00002643001],"domain_scores_gemma":[0.9994429,0.0002422411,0.00005419628,0.00005195346,0.0001795538,0.00002918191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003951535,0.0001644115,0.0194798,0.0001000437,0.00009030756,0.0002515975,0.0002882251,0.9325087,0.003500714,0.003864169,0.0009104606,0.03844637],"study_design_scores_gemma":[0.00005256051,0.00006166191,0.003532953,0.000003189575,0.00001931376,0.00001321752,0.0001004206,0.9943187,0.0006603157,0.0009305731,0.0002951034,0.00001195197],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8934923,0.00004738798,0.09602429,0.0002399142,0.00001174851,0.0002288276,0.0009142072,0.0003994411,0.008641894],"genre_scores_gemma":[0.9743845,0.00002203368,0.02353824,0.00001299092,0.000002894457,0.00005970088,0.000343661,0.00002594316,0.001609903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2630145,"threshold_uncertainty_score":0.5229672,"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."}}