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Record W4410765775 · doi:10.32920/29161043

‘Contaminants of emerging concern’ in wastewater: Are current analytical technologies, policy development and industry guidelines enough to protect human and ecological health?

2025· preprint· en· W4410765775 on OpenAlexfundno aff
Patricia Hania, Kimberley Gilbride, Rania Hamza

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsHuman healthEnvironmental planningBusinessCurrent (fluid)WastewaterEnvironmental scienceEnvironmental resource managementEcologyEnvironmental protectionEnvironmental healthEnvironmental engineeringEngineeringBiologyMedicine

Abstract

fetched live from OpenAlex

The presence of 'contaminants of emerging concern' (CEC) in water sources is a well-documented phenomenon. The term CEC is broadly defined as compounds present in water sources that are not monitored or regulated currently. Research has shown that CEC discharged from wastewater treatment plants (WWTPs) are present in downstream freshwater sources, which are relied upon for drinking water and fish habitat. However, the lack of a CEC regulatory framework for WWTPs combined with the narrow characterization of CEC as discrete chemicals without understanding the cumulative and synergistic impacts of these chemicals upon human and ecological health has resulted in a knowledge gap. Consequently, CEC scientific knowledge has not yet been translated to support the development of evidence-based decision-making tools and legal regulations that could protect freshwater sources, ecosystems, and human health, and be relied upon by municipalities and First Nations that are charged with operating WWTPs.

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)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0070.005
Open science0.0010.001
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.130
GPT teacher head0.410
Teacher spread0.280 · 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 designTheoretical or conceptual
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 routes1
Has abstractyes

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