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Record W2000397838 · doi:10.2166/wst.2011.018

Preliminary study on the occurrence and risk arising from bacteria internalized in zooplankton in drinking water

2010· article· en· W2000397838 on OpenAlexafffund
Françoise Bichai, W.A.M. Hijnen, E.T. Baars, Martine Rosielle, Yolanda Dullemont, Benoît Barbeau

Bibliographic record

VenueWater Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsPolytechnique MontréalNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZooplanktonBacteriaBiologyMicrobiologyEnvironmental chemistryChemistryEcology

Abstract

fetched live from OpenAlex

In this study, an environmental sampling campaign was conducted to detect internalized E. coli and C. jejuni bacteria in zooplankton and amoebae samples collected at various stages of three water treatment plants in Amsterdam, the Netherlands. Eight sampling locations were selected and sampling was performed twice, at a two-week interval, at each location. Chlorination was used to inactivate free (external) bacteria in the concentrated zooplankton samples and sonication was used to disrupt zooplankton organisms in order to release and recover internalized bacteria. Zooplankton enumeration was performed by microscopy. No internalized E. coli or C. jejuni bacteria were recovered from all of the samples analyzed. The occurrence of internalized E. coli or C. jejuni bacteria in drinking water was estimated to be lower than one internalized bacteria in 10⁵ zooplankton organisms, as derived from the detection limit of the sampling campaign. By using the QMRA approach and the Beta-Poisson model, a risk of infection of less than 9.2E-6 and 5.9E-5 was estimated for internalized E. coli and C. jejuni in drinking water, respectively. This study remains preliminary due to the limited number of samples taken at each location.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.252
Teacher spread0.238 · 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

Citations13
Published2010
Admission routes2
Has abstractyes

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