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Evaluation of three full-scale stormwater treatment systems with respect to water yield, pathogen removal efficacy and human health risk from faecal pathogens

2015· article· en· W2172368301 on OpenAlexaff
Susan R. Petterson, Valerie Grace Mitchell, Cheryl M. Davies, James T. O’Connor, Christine Kaucner, David J. Roser, Nicholas J. Ashbolt

Bibliographic record

VenueThe Science of The Total Environment · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsUniversity of Alberta
FundersSmart Water Fund
KeywordsStormwaterPathogenHuman pathogenEnvironmental scienceYield (engineering)Human healthVeterinary medicineEnvironmental engineeringBiologyMicrobiologyEcologyEnvironmental healthMedicineSurface runoffBacteria

Abstract

fetched live from OpenAlex

In this study, three full-scale, operational stormwater harvesting systems located in Melbourne, Australia were evaluated with respect to water yields; pathogen removal performance by analysis of native surrogate data (Escherichiacoli, somatic coliphages and Clostridium perfringens); and potential human health risk associated with exposures to faecal pathogens using Quantitative Microbial Risk Assessment (QMRA). The water yield assessment confirmed variation between design and measured yields. Faecal contamination of urban stormwater was site specific and variable. Different treatment removal performance was observed between each of the microbial surrogates and varied between event and baseline conditions, with negligible removal of viruses during event conditions. Open storages that provide a habitat for waterfowl may lead to elevated risk due to the potential for zoonotic transmission. Nevertheless, in the Australian urban setting studied, the potential for human faecal contamination of the separated stormwater system was a critical driver of risk. If the integrity of the sewerage system can be ensured, then predicted health risks are dramatically reduced.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.056
GPT teacher head0.270
Teacher spread0.214 · 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

Citations33
Published2015
Admission routes1
Has abstractno

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