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Indirect effects of metal contamination on energetics of yellow perch (<i>Perca flavescens</i>) resulting from food web simplification

2005· article· en· W2122783394 on OpenAlexaff
Alison C. Iles, Joseph B. Rasmussen

Bibliographic record

VenueFreshwater Biology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerchPredationBiologyBenthic zoneInvertebrateFood webZooplanktonPercidaeBioaccumulationEnergeticsEcologyTrophic levelBioenergeticsFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Summary 1. Benthic invertebrate community composition and yellow perch ( Perca flavescens ) diet, growth and activity levels from lakes along a metal‐contamination gradient were used to assess the importance of a naturally diverse prey base for maintaining energy transfer to growing fish, and how this transfer is disrupted by metal contamination. 2. Zoobenthic communities had lower diversity in metal‐contaminated lakes, with a notable absence of large bodied invertebrate taxa. 3. The average mass of non‐zooplankton prey items was significantly greater for 2+ and 3+ perch from the reference lake, and increased significantly with age in all except the most contaminated lakes where prey choice was limited. 4. Benthivorous perch from all contaminated lakes exhibited slowed growth. Perch from one of the contaminated lakes exhibited faster growth during piscivory, indicating slowed growth only while benthivorous. 5. Estimates of fish activity, using the activity of the glycolytic enzyme Lactate dehydrogenase in perch white muscle tissue as a proxy, suggested that shifts in diet to larger prey (in reference and intermediately contaminated lakes) lowered activity costs, which may explain how diet shifts maintain growth efficiency as perch grow larger.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.368

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 studies0.0000.000
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 head0.010
GPT teacher head0.218
Teacher spread0.209 · 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

Citations46
Published2005
Admission routes1
Has abstractyes

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