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Record W2114306560 · doi:10.1139/f04-100

Influence of lake chemistry and fish age on cadmium, copper, and zinc concentrations in various organs of indigenous yellow perch (<i>Perca flavescens</i>)

2004· article· en· W2114306560 on OpenAlexvenueno aff
Anik Giguère, Peter G. C. Campbell, Landis Hare, D. G. McDonald, Joseph B. Rasmussen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsnot available
Fundersnot available
KeywordsPerchCadmiumGillZincAnimal scienceJuvenileBioaccumulationBiologyCopperEnvironmental chemistryPercidaeFisheryChemistryFish <Actinopterygii>Ecology

Abstract

fetched live from OpenAlex

Concentrations of Cd, Cu, and Zn were determined in various organs of juvenile yellow perch (Perca flavescens) collected from eight lakes located along a metal concentration gradient. Metal exposure was evaluated on the basis of the free ambient Cd 2+ , Cu 2+ , Zn 2+ , and Ca 2+ concentrations, as estimated from chemical equilibrium simulations using the ambient water chemistry data. Based on regression analyses, lake water Ca 2+ and H + did not significantly influence the steady-state metal concentrations in various body parts of yellow perch. Cadmium concentrations were significantly higher in the gastrointestinal tract than in the gills, suggesting that uptake of this metal from food is more important than uptake from water. Variations in the contribution of each organ to the total Cd and Cu burdens revealed a possible dysfunction in liver excreting capacities in the fish from the most highly contaminated lakes. Additionally, measurements on yellow perch from 0 to 10 years old (N = 81) collected from a single lake suggest that Cd concentrations in some organs increase with fish age, which we interpret to be linked to changes in fish growth rate with age.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

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 studies0.0000.003
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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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.

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

Citations138
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicEnvironmental Toxicology and EcotoxicologyFrench-language works237,207