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

Pharmaceuticals and personal care products in wastewaters

2025· article· en· W4415382699 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
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