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Record W4415382699 · doi:10.56367/oag-048-12224

Pharmaceuticals and personal care products in wastewaters

2025· article· en· W4415382699 on OpenAlexaffabout
Kelly R. Munkittrick, Frederick J. Wrona, Maricor J. Arlos, Mark R. Servos

Bibliographic record

VenueOpen Access Government · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of WaterlooUniversity of Calgary
Fundersnot available
KeywordsEnvironmental impact of pharmaceuticals and personal care productsPersonal careWastewaterAquatic ecosystemEffluentSewage treatmentWater qualityHealth care

Abstract

fetched live from OpenAlex

Pharmaceuticals and personal care products in wastewaters Despite progress in wastewater treatment, PPCPs like medications and personal care products continue to enter ecosystems, threatening aquatic life. Since 2020, the Bow River Ecosystem Health Assessment project in Alberta, Canada, has been evaluating the impact of treated wastewater on the Bow River. Investments to improve wastewater treatment have led to tremendous improvements in water quality and ecosystem health. (1) However, even with tertiary effluent treatment, a diversity of contaminants that include pharmaceuticals and personal care products (PPCPs) continue to enter the environment and have the potential to disrupt the normal growth, development, and reproduction of aquatic organisms. The ongoing development and introduction of new chemicals continuously raise further concerns. The Bow River Ecosystem Health Assessment project has been assessing the impact of treated municipal wastewater discharges from the City of Calgary on the Bow River in central Alberta, Canada, since 2019.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.090
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.003

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 head0.048
GPT teacher head0.384
Teacher spread0.336 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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