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Record W1490380672 · doi:10.2166/wst.2004.0322

Relevance of the sludge retention time (SRT) as design criteria for wastewater treatment plants for the removal of endocrine disruptors and pharmaceuticals from wastewater

2004· article· en· W1490380672 on OpenAlexfundno aff
Norbert Kreuzinger, M. Clara, B. Strenn, H. Kroiß

Bibliographic record

VenueWater Science & Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
FundersEuropean CommissionPublic Health Agency of Canada
KeywordsEffluentWastewaterSewage treatmentActivated sludgeNitrificationEnvironmental sciencePollutantEnvironmental engineeringEnvironmental chemistryChemistryBiologyEcology

Abstract

fetched live from OpenAlex

Wastewater treatment plants (WWTPs) represent a significant source for the input of micro pollutants as endocrine disruptors (EDs) or pharmaceutically active compounds (PhACs) into the aquatic environment. Treatment efficiency of WWTPs often is reported, taking into account only inflow and effluent concentrations without further specification of the WWTP investigated. In order to allow comparison and evaluation of the removal efficiency of different layouts and concepts in wastewater treatment, additional information like the sludge retention time (SRT) and sludge load (F/M ratio) are necessary. Presented results from different WWTPs show correlation of removal of EDs and PhACs to the SRT. Compared to WWTPs with high F/M ratio implementation of the nitrification process on WWTPs results in a significant increase of the removal efficiency for EDs and PhACs. This paper describes an approach to determine comparable removal rates for different activated sludge systems based on mass balance and SRT.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.038
GPT teacher head0.309
Teacher spread0.272 · 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

Citations225
Published2004
Admission routes1
Has abstractyes

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