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Record W2519829251 · doi:10.2166/wqrj.2008.033

Concentrations of Endotoxins in Waters Around the Island of Montreal, and Treatment Options

2008· article· en· W2519829251 on OpenAlexaffabout
Ronald Gehr, Santiago Parent Uribe, Isabel Fatima Da Silva Baptista, Bruce Mazer

Bibliographic record

VenueWater Quality Research Journal · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsChlorineLimulus amebocyte lysateOzoneEnvironmental chemistryChemistryWater treatmentPortable water purificationUltravioletEnvironmental scienceEnvironmental engineeringBiologyLipopolysaccharide

Abstract

fetched live from OpenAlex

Abstract Endotoxins are a component of most Gram negative bacteria, and some cyanobacteria. They may be toxic to humans when inhaled or injected, but the effects are unclear when they are ingested. In fact, low concentrations may protect children against certain allergies. Data for endotoxins in Quebec waters are unavailable, hence this study mapped levels in the waters around Montreal, using two commercial test methods. The recently developed factor C method had a greater linear range and was more convenient to use than the widely used Limulus amebocyte lysate (LAL) method. Although the methods gave endotoxin values of the same order, a consistent relationship between the two could not be established. Endotoxin concentrations in the untreated waters varied from 32 to 1,188 EU/mL, comparable in the literature from pristine waters to wastewaters. Values were generally lower in the summer. Filtration is known to be partially effective at removing endotoxins, but the effects of disinfection are not well established. Accordingly, chlorination, ozonation, and ultraviolet light were tested for the destruction of endotoxins in water, at doses found during drinking water disinfection. While chlorine and ultraviolet light had minimal effects on endotoxin levels, ozone could achieve up to 60% reductions at Ct values (concentration x contact time) as low as 2.5 mg•min/L.

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 score0.006
Threshold uncertainty score0.135

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)0.0000.000

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 head0.147
GPT teacher head0.379
Teacher spread0.232 · 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

Citations21
Published2008
Admission routes2
Has abstractyes

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