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Record W2951746488 · doi:10.82308/32026

Effects of early-life exposure to contaminated sediments in fish

2018· article· en· W2951746488 on OpenAlexfundaboutno aff
Emily Boulanger

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersMcGill University
KeywordsEnvironmental scienceAquatic ecosystemContaminationHatchingPollutantLake ecosystemEnvironmental chemistryFish <Actinopterygii>SedimentFisheryEcosystemEcologyBiologyChemistry

Abstract

fetched live from OpenAlex

Early-life stages of fish are often more susceptible to environmental pollutants than juvenile and adult forms. In aquatic ecosystems, sediments can be an important source of exposure to contaminants in these early-life stage fish. The overall goal of this thesis was to investigate the effects of embryonic and larval exposure of fish to sediments from Lake Saint Louis, a fluvial lake affected by agricultural, industrial, and municipal inputs located in southwestern Quebec.In the first data chapter of this thesis (Ch.2), an in situ method was developed and validated by exposing early-life stages of walleye (Sander vitreus) to reference and contaminated sediments in Lake Saint-Louis, QC. Sites within the lake were characterized as ‘reference’ (1 site) or ‘contaminated’ (3 sites) based on analytical determination of levels of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), dioxins, furans, and metals in lake sediments (data provided by Environment and Climate Change Canada (ECCC)). In order to assess the toxicity of these sediments to early-life stage fish, walleye eggs were placed into hatching boxes and deposited at the 4 sites in Lake Saint Louis in May of 2016 and 2017. A low percentage of the deposited eggs survived (0.9% - 6.6%), likely due to difficulties with the cage design. Live larvae were retrieved from 2 out of 4 sites over the two field seasons. We determined the number of individuals needed for future molecular work (e.g., DNA methylation, gene expression and thiobarbituric acid reactive substances (TBARS) assay which is used to detect oxidative stress). We were able to establish good quality DNA yields of 11-23 µg (5 pooled larvae), and good quality RNA yields of 7-9 µg (3 pooled larvae). A minimum of 30 mg of homogenized walleye larvae was required for TBARS analyses.In the second data chapter of this thesis (Ch. 3), a sediment contact assay was performed, based on the developed methods in Hollert et al. (2003), to assess the potential of contaminated sediments from Lake Saint Louis, QC to cause harm to early-life stages of fish using a model organism, zebrafish (Danio rerio). The sediment contact assay exposed zebrafish from ~3 to 120 hours post-fertilization (hpf) to sediments collected from a reference and two contaminated sites in Lake Saint-Louis, QC. At the end of the study, biochemical (DNA methylation and gene expression), and organismal-level (mortality, abnormalities, hatching rate) effects were evaluated. In comparison to the reference site, mortality was significantly higher in larvae exposed to sediment from one of the contaminated sites (43%), but not the other (21%). Additionally, the expression of 11 genes related to xenobiotic metabolism, embryonic development, oxidative stress or DNA repair were evaluated. Several genes from the aryl hydrocarbon receptor (AhR) pathway were significantly induced by exposure to the contaminated sediment; CYP1A (34, 42-fold induction), CYP1B1 (20, 22-fold induction), AhR2 (1.4, 1.4-fold induction). This study addresses effects of environmentally relevant mixtures of contaminants at concentrations that could be found in the environment, and identified an important molecular pathway for follow up work.

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 score0.034
Threshold uncertainty score0.068

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)0.0010.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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
Published2018
Admission routes2
Has abstractyes

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Same venueeScholarship@McGill (McGill)→Same topicToxic Organic Pollutants Impact→French-language works237,207→