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Record W2787747229 · doi:10.1139/facets-2017-0085

Antimicrobial resistance gene surveillance in the receiving waters of an upgraded wastewater treatment plant

2018· article· en· W2787747229 on OpenAlexaffvenueabout
Claire N. Freeman, Lena Scriver, Kara D. Neudorf, Lisbeth Truelstrup Hansen, Rob Jamieson, Christopher K. Yost

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsDalhousie UniversityUniversity of Regina
Fundersnot available
KeywordsEffluentWastewaterBiologyAbundance (ecology)Sewage treatment16S ribosomal RNAAntibiotic resistanceRelative species abundanceAntimicrobialMicrobial population biologyBiotechnologyEcologyGeneBacteriaEnvironmental scienceMicrobiologyAntibioticsEnvironmental engineeringGenetics

Abstract

fetched live from OpenAlex

Wastewater treatment plants (WWTPs) have been identified as hotspots for antimicrobial resistance genes (ARGs) and thus represent a critical point where patterns in ARG abundances can be monitored prior to their release into the environment. The aim of the current study was to measure the impact of the release of the final treated effluent (FE) on the abundance of ARGs in the receiving water of a recently upgraded WWTP in the Canadian prairies. Sample nutrient content (phosphorous and nitrogen species) was measured as a proxy for WWTP functional performance, and quantitative PCR (qPCR) was used to measure the abundance of eight ARGs, the intI1 gene associated with class I integrons, and the 16S rRNA gene. The genes ermB, sul1, intI1, bla CTX-M , qnrS, and tetO all had higher abundances downstream of the WWTP, consistent with the genes with highest abundance in the FE. These findings are consistent with the increasing evidence suggesting that human activity affects the abundances of ARGs in the environment. Although the degree of risk associated with releasing ARGs into the environment is still unclear, understanding the environmental dimension of this threat will help develop informed management policies to reduce the spread of antibiotic resistance and protect public health.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.312

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 head0.026
GPT teacher head0.263
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 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

Citations25
Published2018
Admission routes3
Has abstractyes

Explore more

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