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Record W3120995809

Dissemination of Antibiotic Resistance Genes into natural environments and Wastewater Treatment Plants - Is there a link?

2017· article· en· W3120995809 on OpenAlexaff
Evans Eshriew, Robin E. Owen

Bibliographic record

VenueURSCA Proceedings · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsMount Royal University
Fundersnot available
KeywordsAntibioticsSewage treatmentSewageAntibiotic resistanceWastewaterResistance (ecology)BiologyNatural (archaeology)BacteriaBiotechnologyEcologyMicrobiologyEnvironmental engineeringEnvironmental scienceGenetics
DOInot available

Abstract

fetched live from OpenAlex

The misuse of antibiotics has led to the emergence and spread of antibiotic resistant bacteria (ARB), which is of great concern to public health. Normally, wastewaters containing ARB originated from humans and animals are processed in wastewater treatment plants (WWTPs). Although it has been demonstrated that most WWTPs effectively and efficiently remove harmful bacteria and antibiotics from sewage waters before their release into local natural environments, recent molecular-based studies have revealed the unexpected presence of high levels of clinically-relevant antibiotic resistance genes (ARGs) in treated waters. We are conducting a research of the literature to better understand the connections between operations in WWTPs and the dissemination of antibiotic resistance genes (ARGs) into natural environments. Taking cases from different parts of the world, we are analyzing reports describing ARGs originally implicated in hospital infections, such as those encoding for extended spectrum beta lactamases (ESBL), and their fate in locations surrounding water treatment facilities. Also, we are reviewing the current understanding regarding risk management to limit the potential dissemination of ARGs to natural ecosystems via water treatment facilities. Preliminary results of these analyses will be presented. * Indicates faculty mentor.

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 score0.135
Threshold uncertainty score0.596

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 studies0.0000.001
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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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