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Record W2142926429 · doi:10.1139/f01-045

Isotopic turnover in aquatic predators: quantifying the exploitation of migratory prey

2001· article· en· W2142926429 on OpenAlexvenueno aff
Stephen E. MacAvoy, Stephen A. Macko, Greg C. Garman

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersVirginia Commonwealth University
KeywordsIctalurusAlosaFish migrationAlewifeBiologyCatfishPredationFisheryHerringEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

In the tidal freshwaters of Virginia, U.S.A., the blue catfish (Ictalurus furcatus), an introduced piscivore, derives a significant proportion of its nutrition from spawning anadromous fish (genus Alosa, including blueback herring (A. aestivalis), American shad (A. sapidissima), and alewife (A. pseudoharengus)). Because the Alosa are not continually available to I. furcatus, there is an isotopic turnover, defined as change in isotope composition due to growth and metabolic tissue replacement, in I. furcatus tissues associated with the diet switch from freshwater to anadromous fishes. However, isotopic turnover rates for ictalurid fish are unknown. This study determined the maximum isotopic turnover rate of channel catfish (Ictalurus punctatus) tissues and compared this maximum rate with that of I. furcatus captured in the field over the 3-month Alosa spawning run. Maximum turnover rates for δ 13 C were 0.014 and 0.017‰ per day in muscle and blood. For δ 34 S, rates were 0.017 and 0.020‰ per day in muscle and blood, respectively. Isotopic turnover of muscle carbon reflected growth rate, but sulfur did not match growth as well. Ictalurus furcatus captured in the field showed no enrichment during the Alosa spawning run owing to slow turnover and variable diet. In aquatic ecosystems that have migrating prey, exploitation by predators may be underestimated using isotopes because of slow tissue turnover.

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 score0.329
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.241
Teacher spread0.212 · 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

Citations188
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicIsotope Analysis in EcologyFrench-language works237,207