{"id":"W2886991718","doi":"10.1128/aem.01634-18","title":"Microbial Source Tracking Using Quantitative and Digital PCR To Identify Sources of Fecal Contamination in Stormwater, River Water, and Beach Water in a Great Lakes Area of Concern","year":2018,"lang":"en","type":"article","venue":"Applied and Environmental Microbiology","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Contamination; Fecal coliform; Stormwater; Source tracking; Indicator bacteria; Environmental science; Watershed; Digital polymerase chain reaction; Outfall; Water quality; Sewage; Feces; Estuary; Hydrology (agriculture); Biology; Ecology; Surface runoff; Environmental engineering; Polymerase chain reaction; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002226683,0.0001535755,0.0002689898,0.00008030712,0.00005397168,0.00001749815,0.00006821405,0.0001026787,0.00006932743],"category_scores_gemma":[0.000003588801,0.0001136042,0.00001798297,0.00003508364,0.001183667,0.0001534392,0.0002574758,0.00007478968,0.000008301536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007042482,"about_ca_system_score_gemma":0.000001261415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003238065,"about_ca_topic_score_gemma":0.0002581078,"domain_scores_codex":[0.9989976,0.00006108682,0.000312653,0.0003394623,0.00005069891,0.000238478],"domain_scores_gemma":[0.9997854,0.00003676432,0.00005746727,0.00006994184,0.000002497401,0.00004788573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000959988,0.00005919556,0.1522407,0.00001327946,0.000007034333,0.000001425353,0.01357513,0.00001932563,0.8307386,0.00001031404,0.00000203572,0.003236999],"study_design_scores_gemma":[0.001669042,0.0002806868,0.2294732,0.00003179216,0.00001411697,0.00003163608,0.002285552,0.0001773934,0.7651873,0.0001120626,0.00049173,0.0002454659],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999319,0.0000346534,0.0002491956,0.00003328402,0.00002276537,0.0002446767,0.00004679613,0.000004183256,0.00004541666],"genre_scores_gemma":[0.999634,0.000008151899,0.0001339268,0.00006580733,0.00000674337,0.000004658678,0.0000866542,0.000009291494,0.00005076879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07723259,"threshold_uncertainty_score":0.4632645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515458562384181,"score_gpt":0.2510339846733712,"score_spread":0.2258793990495294,"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."}}