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Effects of pollution and parasites on biomarkers of fish health in spottail shiners <i>Notropis hudsonius</i> (Clinton)

2007· article· en· W2135893331 on OpenAlexaffabout
I. D. S. I. P. Thilakaratne, John McLaughlin, David J. Marcogliese

Bibliographic record

VenueJournal of Fish Biology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsConcordia UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsNotropisBiologyParasite hostingFish <Actinopterygii>ZoologyPollutionDigeneaEcologyAbundance (ecology)FisheryHelminthsTrematoda

Abstract

fetched live from OpenAlex

Seven biomarkers in 204 spottail shiners Notropis hudsonius were examined for effects of pollution and parasites on fish health at localities along the St Lawrence River, Canada. The number of pigmented macrophage centres and pigmented macrophages in the spleen was significantly higher at polluted localities receiving urban and industrial effluents than at reference localities, indicating that they were good indicators of exposure to pollution in spottail shiners. Seven of the nine species of parasites found in 1+ year fish showed significant correlations with biomarkers. More parasites (18 species) but fewer correlations with biomarkers were observed in 2+ year fish, indicating that parasite effects were more pronounced in young spottail shiners. A significant negative relationship was observed between condition factor and Neoechinorhynchus rutili in 1+ year fish, suggesting its potential pathological significance in young spottail shiners. High abundance of Plagioporus sinitsini was associated with higher spleen macrophage counts and lower indices of condition at polluted localities. Furthermore, infection by P. sinitsini in polluted conditions appeared to have a greater negative effect on fish health than either stressor alone, providing further evidence that parasites should be considered when examining effects of pollution on fish health.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.150
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.320
Teacher spread0.311 · 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 teacher head, 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

Citations47
Published2007
Admission routes2
Has abstractyes

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