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Record W2169372032 · doi:10.2166/wqrj.2009.017

Reduction of Alkali-Labile Phosphates in Mussels Exposed to Primary-Treated Wastewaters Undergoing Ozone and Ultraviolet Disinfection: A Pilot Study

2009· article· en· W2169372032 on OpenAlexaff
François Gagné, C. André, P. Cejka, C. Blaise, Robert Häusler

Bibliographic record

VenueWater Quality Research Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsÉcole de Technologie SupérieureEnvironment and Climate Change Canada
Fundersnot available
KeywordsEffluentChemistryOzoneEnvironmental chemistryPrimary (astronomy)PhosphateEnvironmental engineeringEnvironmental scienceBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to examine the estrogenic potential of a primary municipal effluent, undergoing ozone or ultraviolet disinfection, in Elliptio complanata mussels. Mussels were exposed for seven weeks, using a continuous flowthrough system, to a primary effluent from a major city before and after disinfection. Results showed that the effluents, regardless of the disinfection procedure, readily affected gametogenesis in mussels as determined by gonado-somatic index, and by DNA synthesis as determined by the activity of aspartate transcarbamoylase activity, a rate-limiting enzyme for pyrimidine synthesis. The estrogenic potential of the effluents was observed by the increased levels of alkali-labile phosphate, a generic measure for vitellogenin-like proteins, where the disinfection procedures did not completely remove the estrogenic effects of the primary effluent. Thus, the introduction of an ozone or ultraviolet disinfection step to a primary-treated effluent will likely diminish the estrogenic potential, but not entirely.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.113
GPT teacher head0.388
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2009
Admission routes1
Has abstractyes

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