MétaCan
Menu
Back to cohort
Record W2407272075 · doi:10.2175/106143007x221085

Denitrification with Carbon Addition—Kinetic Considerations

2008· article· en· W2407272075 on OpenAlexaff
Peter Dold, I. Takács, Yalda Mokhayeri, Adam Nichols, Jeneva Hinojosa, Rumana Riffat, Charles Bott, W. Bailey, Sudhir Murthy

Bibliographic record

VenueWater Environment Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnviroSim (Canada)
Fundersnot available
KeywordsHeterotrophMethanolEffluentChemistryPulp and paper industryWastewaterDenitrificationCarbon fibersNitrogenSewage treatmentEnvironmental engineeringSugarEnvironmental chemistryEnvironmental scienceOrganic chemistryBiologyMaterials scienceBacteria

Abstract

fetched live from OpenAlex

The Blue Plains Advanced Wastewater Treatment Plant (Washington, D.C.) uses methanol as an external carbon source in a postdenitrification process, to achieve low effluent total nitrogen concentrations. This becomes more difficult in winter, at lower mixed liquor temperatures and higher flows, as a consequence of the kinetic behavior of the methanol-utilizing heterotrophs. The paper reports on an experimental batch test study conducted on Blue Plains postdenitrification sludge to investigate (1) the maximum specific growth rate of methanol-utilizing heterotrophs (Mu(METH)); (2) the temperature dependency of the growth rate; and (3) the efficacy of alternate substrates (ethanol, acetate, and sugar). A limited number of tests were conducted on sludge from two other treatment plants with methanol addition.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.254
Teacher spread0.203 · 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 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

Citations38
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueWater Environment ResearchSame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207