{"id":"W2046806498","doi":"10.1016/j.watres.2013.09.031","title":"Influence of seasonal and inter-annual hydro-meteorological variability on surface water fecal coliform concentration under varying land-use composition","year":2013,"lang":"en","type":"article","venue":"Water Research","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Ministry of Environment; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Public Health Agency of Canada","keywords":"Snowmelt; Environmental science; Fecal coliform; Surface runoff; Hydrology (agriculture); Precipitation; Indicator bacteria; Snow; Surface water; Contamination; Water quality; Glacier; Physical geography; Environmental engineering; Ecology; Meteorology; Geography","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001908134,0.0001172068,0.0001538008,0.00002442574,0.0001725911,0.0001012407,0.0001636244,0.0001075851,0.001785086],"category_scores_gemma":[0.00005205885,0.00006741183,0.00003078262,0.00007593438,0.0005526654,0.0006261332,0.0003423654,0.0003031955,0.0007825939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001693885,"about_ca_system_score_gemma":0.000005845416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070748,"about_ca_topic_score_gemma":0.00001945652,"domain_scores_codex":[0.9975202,0.0008085611,0.0002639813,0.000356963,0.0006055161,0.0004447838],"domain_scores_gemma":[0.999375,0.0001659814,0.00002251107,0.0001920886,0.00009183469,0.0001525602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003098974,0.0003422514,0.367239,0.00003979637,0.0000162869,0.000004391769,0.002004305,0.00681348,0.6220737,0.0002607849,0.0003635974,0.0005324655],"study_design_scores_gemma":[0.0007483181,0.0004828746,0.6614255,0.00002294581,0.000005356535,0.000005294478,0.0000471412,0.01138233,0.3220441,0.003298678,0.0003470608,0.0001904106],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972437,7.882374e-7,0.0001868269,0.001533147,0.00001759511,0.0004553409,0.00001679671,0.00002157832,0.0005242362],"genre_scores_gemma":[0.9993018,0.000001933166,0.000101653,0.0002419968,0.00001420074,0.0000213854,0.0000491248,0.000006680118,0.0002612052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3000296,"threshold_uncertainty_score":0.9999954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0432340965266157,"score_gpt":0.3112653292051885,"score_spread":0.2680312326785728,"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."}}