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Record W4406890536 · doi:10.1021/acs.est.4c09663

Strengthening Policy Relevance of Wastewater-Based Surveillance for Antimicrobial Resistance

2025· article· en· W4406890536 on OpenAlexaffabout
Sheena Conforti, Amy Pruden, Nicole Acosta, Christopher W. N. Anderson, Helmut Bürgmann, Juliana Calábria de Araújo, Judith R. Cristobal, Barbara Drigo, Claire M. Ellison, Zanah Francis, Dominic Frigon, Julia Vierheilig, Timothy R. Julian, Uli Klümper, Liping Ma, Chand S. Mangat, Maya Nadimpalli, Manami Nakashita, Gilbert Osena, Sasikaladevi Rathinavelu, Richard J. Reid‐Smith, Michael A. Saldana, Heike Schmitt, Shuxian Li, Andrew C. Singer, Tam T. Tran, Kadir Yanaç, Gustavo Ybazeta, Monika Harnisz

Bibliographic record

VenueEnvironmental Science & Technology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsHealth Sciences NorthUniversity of ManitobaPublic Health Agency of CanadaUniversity of CalgaryMcGill UniversityQueen's UniversityUniversity of Alberta
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsRelevance (law)WastewaterAntibiotic resistanceAntimicrobialResistance (ecology)Waste managementEnvironmental scienceEnvironmental engineeringMicrobiologyEngineeringPolitical scienceAntibioticsBiologyEcology

Abstract

fetched live from OpenAlex

A ntimicrobial resistance (AMR) is among the top 10 public health threats, with nearly 5 million deaths in 2019 linked to AMR-related bacterial infections. 1 A One Health approach is needed to combat AMR.Healthcare-based surveillance (HBS) of AMR provides incomplete information about the scope of the AMR threat.HBS screens only patients seeking medical attention, lacking community-level representativeness, and suffers from underreporting. 2 Consequently, researchers are turning to wastewater-based surveillance (WBS) to complement HBS. 3 WBS can provide information about AMR circulating within communities and hospitals, offering a comprehensive understanding of AMR prevalence.However, the surveillance targets and data obtained from WBS are distinct from those derived from HBS, creating uncertainty regarding their utility to the public health sector and ability to yield policy relevant information.In May 2024, participants in a workshop during the 7 th Environmental Dimension of Antimicrobial Resistance (EDAR7) conference (Montreál, Canada) sought to answer

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 categoriesScience and technology studies
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 score0.026
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.224
Teacher spread0.220 · 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.

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

Citations20
Published2025
Admission routes2
Has abstractyes

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