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Record W4293446415 · doi:10.1128/spectrum.02473-22

Spatiotemporal Investigation of Antibiotic Resistance in the Urban Water Cycle Influenced by Environmental and Anthropogenic Activity

2022· article· en· W4293446415 on OpenAlexaff
Keira Tucker, Leonardos Mageiros, Alno Carstens, Ludwig Bröcker, Edward Archer, Katrin Smith, Evangelos Mourkas, Ben Pascoe, Daan Nel, Guillaume Méric, Samuel K. Sheppard, Barbara Kasprzyk‐Hordern, Marelize Botes, Edward J. Feil, Gideon Wolfaardt

Bibliographic record

VenueMicrobiology Spectrum · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsToronto Metropolitan University
FundersMedical Research CouncilNational Research FoundationEngineering and Physical Sciences Research CouncilGlobal Challenges Research Fund
KeywordsResistance (ecology)Environmental scienceAntibiotic resistanceAntibioticsEnvironmental healthGeographyEcologyMedicineBiologyMicrobiology

Abstract

fetched live from OpenAlex

The results from this study are of importance as they fill a gap in the data available on environmental AMR in South Africa to date. This study was done in parallel with co-investigators focusing on the prevalence of various antimicrobials at the same sites selected in our study, verifying that the sites that are influenced by informal settlements and WWTW influent had higher concentrations of antimicrobials and antimicrobial metabolites. The various locations of the sample sites selected, the frequency of the samples collected over a year, and the different types of samples collected at each site all contribute to informing how AMR in the environment might be affected by anthropogenic activity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2022
Admission routes1
Has abstractyes

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