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Record W15299644 · doi:10.2175/106143006x95401

Effects of Nitrogen and Phosphorus Limitation on the Activated Sludge Biomass in a Kraft Mill Biotreatment System

2006· article· en· W15299644 on OpenAlexaff
Jasmine Bhathena, Brian T. Driscoll, Trevor C. Charles, Frederick S. Archibald

Bibliographic record

VenueWater Environment Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsMcGill University
Fundersnot available
KeywordsChemistryNitrogenPhosphorusActivated sludgePulp and paper industryEffluentEnhanced biological phosphorus removalExtracellular polymeric substanceWastewaterPaper millMixed liquor suspended solidsPulp millKraft processPulp (tooth)BioreactorChemical oxygen demandSewage treatmentBiomass (ecology)Environmental chemistryKraft paperEnvironmental engineeringEnvironmental scienceAgronomyOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Unlike wastewater, pulp and paper mill effluents are generally severely deficient in bioavailable nitrogen and phosphorus. The influence of nitrogen and phosphorus limitations on steady-state or typical pulp and paper mill activated sludge floc properties and performance was studied using a bioreactor-fed synthetic raw mill effluent and seeded with mill activated sludge. Limitation of either nitrogen or phosphorus decreased growth, five-day biochemical oxygen demand, and suspended solids removal. Nitrogen limitation greatly enhanced activated sludge floc poly-beta3-hydroxybutyrate (PHB), but not carbohydrate or extracellular polymeric substances (EPS). In contrast, phosphorus limitation increased total floc carbohydrate and EPS, but not PHB. The flocs showed little ability to store either nitrogen or phosphorus. Nitrogen limitation, but not phosphorus limitation, produced much more negative net floc surface charge, increasing fines, while phosphorus limitation, but not nitrogen limitation, increased the floc bound water content and surface hydrophobicity and decreased fines.

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 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 score0.024
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.231
Teacher spread0.215 · 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 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

Citations22
Published2006
Admission routes1
Has abstractyes

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