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National scale assessment of the occurrence and risk of trace organic contaminants in Canadian Lake sediments

2025· article· en· W4406687413 on OpenAlexafffundabout
François-Xavier Teysseire, Hubert Cabana, Yannick Huot, Pedro A. Segura

Bibliographic record

VenueThe Science of The Total Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental scienceTRACE (psycholinguistics)ContaminationEnvironmental chemistryScale (ratio)GeographyChemistryEcologyCartographyBiology

Abstract

fetched live from OpenAlex

observed for DEET in one lake. The geographic distribution of analyte concentrations varied by compound class: pharmaceuticals and consumer product additives were predominantly found in the more urbanized regions of Ontario and Quebec, whereas personal care products such as DEET and oxybenzone were more frequently detected in the western provinces of Canada. An environmental risk assessment based on an additive model conducted on three aquatic organisms (algae, cladocerans, and fish) revealed that 4 % and 6 % of the lakes posed a potentially high risk for cladocerans and algae, respectively. A geographical analysis indicated that lakes in the south of the eastern provinces of Canada presented the highest risks for all three species. These findings represent the first large-scale results detailing the extent of contamination caused by TrOCs on Canadian lake sediments. They establish reference levels that can guide future monitoring efforts and inform policy discussions aimed at protecting lake ecosystems.

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 score0.023
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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