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Record W2071039965 · doi:10.1080/09603123.2012.678001

Contamination of public whirlpool spas: factors associated with the presence of<i>Legionella</i>spp.,<i>Pseudomonas aeruginosa</i>and<i>Escherichia coli</i>

2012· article· en· W2071039965 on OpenAlexaffabout
Nicholas Brousseau, Benoît Lévesque, Thibault A. Guillemet, Philippe Cantin, Denis Gauvin, J. Giroux, Suzanne Gingras, François Proulx, Pierre-André Côté, Éric Dewailly

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsCentre hospitalier de l'Université LavalUniversité de SherbrookeUniversité LavalInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsLegionellaPseudomonas aeruginosaContaminationEscherichia coliMicrobiologyTurbidityChlorineBacteriaFood scienceChemistryVeterinary medicineBiologyMedicineEcology

Abstract

fetched live from OpenAlex

This work explores the factors associated with contamination of public spas by Legionella spp., Pseudomonas aeruginosa and Escherichia coli. Physicochemical and microbiological parameters were measured in water samples from 95 spas inQuébec, Canada. Spa maintenance was documented by a questionnaire. Legionella spp. were detected in 23% of spas, P. aeruginosa in 41% and E. coli in 2%. Bacteria were found in concerning concentrations (Legionella spp. ≥ 500 CFU/l, P. aeruginosa ≥ 51 CFU/100 ml or E. coli ≥ 1 CFU/100 ml) in 26% ofspas. Observed physicochemical parameters frequently differed from recommended guidelines. The following factors decreased the prevalence of concerning microbial contamination: a free chlorine concentration ≥ 2 mg/l or total bromine ≥ 3 mg/l (p = 0.001), an oxidation-reduction potential (ORP) > 650 mV (p = 0.001), emptying and cleaning the spa at least monthly (p = 0.019) and a turbidity ≤ 1 NTU (p = 0.013). Proper regulations and training of spa operators are critical for better maintenance of these increasingly popular 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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.062
GPT teacher head0.336
Teacher spread0.274 · 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 designObservational
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

Citations19
Published2012
Admission routes2
Has abstractyes

Explore more

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