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Record W4388204496 · doi:10.3390/toxins15110628

The Effect of Microcystis on the Monitoring of Faecal Indicator Bacteria

2023· article· en· W4388204496 on OpenAlexafffund
Jingjing Zhou, Mingzhi Qu, Christy Dunkinson, Daniel D. Lefebvre, Yuxiang Wang, R. Stephen Brown

Bibliographic record

VenueToxins · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilOntario Centres of Excellence
KeywordsMicrocystisBacteriaEscherichia coliMicrocystis aeruginosaMicrobiologyBiologyMicrocosmMicrocystinCyanobacteriaIndicator bacteriaEnterobacteriaceaeFood scienceFecal coliformEcologyBiochemistryWater quality

Abstract

fetched live from OpenAlex

The survival of Escherichia coli (E. coli) bacteria, the most common faecal indicator bacteria (FIB), may be significantly affected by cyanobacteria present during a harmful algal bloom (HAB). Therefore, the effect of Microcystis on the survival of FIB E.coli and coliforms was investigated. Microcosms containing two species of Microcystis (M. aeruginosa and M. smithii) were established and then inoculated with four reference strains of E. coli (ATCC 25922, 8739, 51813, and 11775) to explore the cyanobacteria-bacteria dynamics at a laboratory setting. Monitoring over several days showed normal growth of Microcystis, with or without the presence of E. coli. However, Microcystis was shown to dramatically decrease the survival of E. coli over time. Analysis of microcystin production by Microcystis was found to correlate with loss of E. coli, suggesting a toxic effect of microcystins on E. coli bacteria. This phenomenon was also demonstrated for a natural consortium of E. coli and coliform bacteria by inoculating with contaminated lake water. The results indicate that the use of E. coli as FIB may be greatly compromised in the presence of Microcystis spp. such as during a HAB when associated toxins are produced.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.474

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 integrity0.0000.000
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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