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Record W2081640397 · doi:10.2166/wqrjc.2012.032

Removal of individual sterols during secondary treatment of pulp mill effluents

2012· article· en· W2081640397 on OpenAlexaff
Zahid Khan, Eric R. Hall

Bibliographic record

VenueWater Quality Research Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStigmasterolCampesterolEffluentSterolChemistryErgosterolPaper millPulp and paper industryAerated lagoonChromatographyActivated sludgeBotanySewage treatmentWaste managementCholesterolBiologyBiochemistry

Abstract

fetched live from OpenAlex

Analysis of plant sterols in pulp mill effluents is complex and several investigations have combined wood extractives with sterols or have reported total sterols collectively. The present study exclusively focuses on the fate of individual sterols generated in pulp and paper manufacturing as they pass through activated sludge biotreatment system. Before secondary treatment the pulp mill effluents contained 33% ß-sitosterol, 26% campesterol, 22% ß-sitostanol, 17% stigmasterol and 2% ergosterol (1,000–1,800 μg/L mean total sterols). After treatment, the effluents contained 44% ß-sitosterol, 22% campesterol, 18% ß-sitostanol, 14% stigmasterol and 2% ergosterol (176–428 μg/L mean total sterols). Each sterol fraction showed different removal efficiency. ß-Sitosterol, the major fraction, was removed relatively poorly (65%) while campesterol was removed most efficiently (81%) compared with the removal of other fractions (ß-sitostanol 74% and stigmasterol 64%). The differential removal of sterol fractions altered the sterol profile during different stages of the secondary treatment. Owing to its poor removal, ß-sitosterol is the most persistent fraction in treated/untreated pulp mill effluents. Typically, 21% of the incoming sterols were contained in secondary effluents and 23% in waste sludge without biodegradation. Optimizing the design/operation of treatment systems for removal of ß-sitosterol and stigmasterol would improve the performance of effluent treatment facilities.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.189
GPT teacher head0.438
Teacher spread0.249 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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