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Record W2070651209 · doi:10.1139/f02-095

Effects of environmental metal contamination on the condition, swimming performance, and tissue metabolic capacities of wild yellow perch (<i>Perca flavescens</i>)

2002· article· en· W2070651209 on OpenAlexfundvenueaboutno aff
James W. Rajotte, Patrice Couture

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
FundersMinistry of Natural Resources
KeywordsPerchBioenergeticsAnaerobic exerciseCadmiumLactate dehydrogenaseBiologyPercidaeCitrate synthaseDehydrogenaseMetabolismEnzymeEnvironmental chemistryChemistryBiochemistryFish <Actinopterygii>FisheryMitochondrionPhysiology

Abstract

fetched live from OpenAlex

In this study, the general condition, swimming capacities, and tissue metabolic capacities and metal concentrations in wild yellow perch (Perca flavescens) were examined from a range of metal-contaminated lakes near Sudbury, Ontario. Fish exposed to elevated environmental cadmium and copper concentrations showed higher liver concentrations of these metals and lower condition indices. Because growth rate appeared lower in the most metal-contaminated fish, the high tissue activities of nucleoside diphosphate kinase, an indicator of biosynthesis, in these fish indicated an increased rate of protein turnover and suggested a bioenergetic cost of metal exposure. Yellow perch from the most metal-contaminated lakes exhibited lower aerobic capacities, as indicated by citrate synthase and β-hydroxyacyl coenzyme A dehydrogenase activities, two mitochondrial enzymes involved in aerobic adenosine triphosphate production, and by critical swimming speed. There was no evidence from our data that environmental metal exposure affected anaerobic capacities of tissues as measured by lactate dehydrogenase activities or anaerobic fast-start swimming performance. Overall, these data show that metal exposure leads to measurable effects on metabolic capacities in wild yellow perch.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.012
GPT teacher head0.178
Teacher spread0.166 · 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 designBench or experimental
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

Citations118
Published2002
Admission routes3
Has abstractyes

Explore more

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