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Record W2133444399 · doi:10.1002/jctb.750

Control of filamentous organisms in food‐processing wastewater treatment by intermittent aeration and selectors

2003· article· en· W2133444399 on OpenAlexaff
George Nakhla, Andrew Lugowski

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsConestoga CollegeWestern University
Fundersnot available
KeywordsAerationSettlingWastewaterAnoxic watersActivated sludgeSewage treatmentPulp and paper industryMicroorganismChemistryVolume (thermodynamics)Food industryEnvironmental engineeringEnvironmental scienceWaste managementFood scienceEnvironmental chemistryBiologyBacteriaEngineering

Abstract

fetched live from OpenAlex

Abstract Various measures were tested at a full‐scale wastewater treatment plant to control sludge bulking by type 0041 and 0675 filamentous microorganisms, instigated by highly variable wastewater loadings from a food‐processing facility. Intermittent aeration on a 1‐h on 1‐h off basis was found to effect a marginal improvement in sludge settling characteristics, as reflected by about an 11–36% reduction in the Sludge Volume Index (SVI) to 118 cm −3 g −1 . At BOD loadings of 1500 kg d −1 which marginally exceeded the design capacity of the plant of 1200 kg d −1 , SVI rose sharply to 230 cm −3 g −1 in less than a week. The anoxic selector effected a reduction in SVI to 170 cm −3 g −1 within 3 weeks of operation at temperatures of 8–12 °C. The aerobic selector was most effective, reducing SVIs further to 79 cm −3 g −1 in 2 weeks. Sludge settleablity was found to be inversely proportional to the aerobic selector food‐to‐microorganism ratio. The optimum aerobic selector loading was found to be 1.8–2.7 kgBOD 5 kgMLVSS d −1 , with corresponding SVIs in the range of 80–120 cm −3 g −1 . © 2003 Society of Chemical Industry

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)0.000
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 score0.014
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

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 integrity0.0010.000
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations11
Published2003
Admission routes1
Has abstractyes

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