MétaCan
Menu
Back to cohort
Record W2087505528 · doi:10.2166/wst.2010.975

Uncertainty and variability in enhanced biological phosphorus removal (EBPR) stoichiometry: consequences for process modelling and optimization

2010· article· en· W2087505528 on OpenAlexaff
Dwight Houweling, Yves Comeau, Imre Takács, Peter Dold

Bibliographic record

VenueWater Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsPolytechnique MontréalEnviroSim (Canada)
Fundersnot available
KeywordsEnhanced biological phosphorus removalPhosphorusActivated sludgeAnaerobic exerciseChemistryPolyphosphatePopulationStoichiometrySequencing batch reactorPulp and paper industryWastewaterEnvironmental chemistryEnvironmental engineeringEnvironmental scienceBiologyBiochemistryPhosphatePhysiology

Abstract

fetched live from OpenAlex

The overall potential for enhanced biological phosphorus removal (EBPR) in the activated sludge process is constrained by the availability of volatile fatty acids (VFAs). The efficiency with which polyphosphate accumulating organisms (PAOs) use these VFAs for P-removal, however, is determined by the stoichiometric ratios governing their anaerobic and aerobic metabolism. While changes in anaerobic stoichiometry due to environmental conditions do affect EBPR performance to a certain degree, model-based analyses indicate that variability in aerobic stoichiometry has the greatest impact. Long-term deterioration in EBPR performance in an experimental SBR system undergoing P-limitation can be predicted as the consequence of competition between PAOs and GAOs. However, the observed rapid decrease in P-release after the change in feed composition is not consistent with a gradual shift in population.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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.

Study designBench or experimental
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

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueWater Science & TechnologySame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207