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Record W2964895499 · doi:10.2166/wh.2019.054

Precipitation effects on parasite, indicator bacteria, and wastewater micropollutant loads from a water resource recovery facility influent and effluent

2019· article· en· W2964895499 on OpenAlexaff
Samira Tolouei, Laurène Autixier, Milad Taghipour, Jean‐Baptiste Burnet, Jane Bonsteel, Sung Vo Duy, Sébastien Sauvé, Michèle Prévost, Sarah Dorner

Bibliographic record

VenueJournal of Water and Health · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsUniversité de MontréalPolytechnique MontréalNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsEffluentEnvironmental scienceWastewaterIndicator bacteriaCryptosporidiumFecal coliformSewage treatmentPrecipitationGiardiaEnvironmental engineeringBiologyWater qualityEcologyFecesMicrobiology

Abstract

fetched live from OpenAlex

The variability of fecal microorganisms and wastewater micropollutants (WWMPs) loads in relation to influent flow rates was evaluated for a water resource recovery facility (WRRF) in support of a vulnerability assessment of a drinking water source. Incomplete treatment and bypass discharges often occur following intense precipitation events that represent conditions that deviate from normal operation. Parasites, fecal indicator bacteria, and WWMPs concentrations and flow rate were measured at the WRRF influent and effluent during dry and wet weather periods. Influent concentrations were measured to characterize potential bypass concentrations that occur during wet weather. Maximum influent Giardia and C. perfringens loads and maximum effluent Escherichia coli and C. perfringens loads were observed during wet weather. Influent median loads of Cryptosporidium and Giardia were 6.8 log oocysts/day and 7.9 log cysts/day per 1,000 people. Effluent median loads were 3.9 log oocysts/day and 6.3 log cysts/day per 1,000 people. High loads of microbial contaminants can occur during WRRF bypasses following wet weather and increase with increasing flow rates; thus, short-term infrequent events such as bypasses should be considered in vulnerability assessments of drinking water sources in addition to the increased effluent loads during normal operation following wet weather.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.248
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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