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Application of a new approach method to assess the hazard of complex legacy contaminated groundwater mixtures on fathead minnows in outdoor mesocosms

2024· article· en· W4402313492 on OpenAlexafffund
Laura Gasque-Belz, Kailey Carrière, Braedon W. Humeniuk, Bradley Park, Carly Colville, Steven D. Siciliano, Natacha Hogan, Lynn P. Weber, Patrick Campbell, Rachel Peters, Markus Hecker, Mark L. Hanson

Bibliographic record

VenueThe Science of The Total Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFederated Co-operatives (Canada)Nordion (Canada)University of SaskatchewanUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMesocosmEnvironmental scienceHazardContaminationGroundwaterHazard analysisEnvironmental hazardGroundwater contaminationWater resource managementEnvironmental engineeringEnvironmental chemistryGeologyEngineeringEcologyAquiferGeotechnical engineeringReliability engineeringEcosystemChemistryBiology

Abstract

fetched live from OpenAlex

Assessing the environmental risks of contaminated groundwater presents significant challenges due to its often-complex chemical composition and to dynamic processes affecting exposure of organisms in receiving surface waters. The objective of this study was to characterize the effects of groundwater collected from a legacy contaminated industrial site, in fish under environmentally relevant conditions. A 21-day fish short-term reproduction assay was conducted in outdoor wetland mesocosms by exposing adult fathead minnows ( Pimephales promelas ) to graded concentrations of groundwater (1 %, 3 %, and 6 %). Offspring were held in mesocosms up to four days post-hatch to apply a new approach method (NAM), the EcoToxChip™, to explore whether traditional apical endpoints could be predicted using an alternative mechanistic approach. None of the groundwater concentrations used in this study were lethal to fish. There was greater cumulative number of eggs produced at the highest concentration of exposure. However, no abnormal histological appearance was observed in the liver and gonads of fish and no significant effect was observed in the relative expression of genes, tubercle counts, and erythrocyte micronuclei counts compared to the negative control. Food availability in the mesocosms was also assessed and the abundance of zooplankton increased in all groundwater-treated mesocosms. Fathead minnow findings are in contrast to those obtained from previous controlled laboratory studies that revealed significant genotoxicity, hepatotoxicity, and reprotoxicity of the same mixtures. Several factors could explain these observations, including the aging of groundwater in mesocosms before fish addition resulting in photo- and biodegradation and binding to sediments of toxic components. Our static exposure scenario likely underestimated realistic exposure scenarios where groundwater inflow to surface water is generally semi-continuous. Nevertheless, focused transcriptome analysis using EcoToxChips also observed greater toxicity during previous laboratory tests compared to mesocosm scenarios, and thus, our results support the use of this NAM in the ecological risk assessment of contaminated groundwater. Outcomes from laboratory exposure overestimated the range and magnitude of those obtained after mesocosm exposure. Molecular outcomes were reflective of apical outcomes under both laboratory and mesocosm scenarios. • Groundwater mixtures in mesocosms did not induce repro-, hepato- or genotoxicity in fish. • Groundwater mixtures did not induce transcriptomic effects in fish. • Laboratory outcomes overestimated the more environmentally realistic scenario. • Molecular outcomes were reflective of apical effects. • Laboratory ‘omics analyses can inform ecological risk assessment.

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)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.284
Teacher spread0.250 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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