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Record W2057951639 · doi:10.1139/f08-206

Mucus: a new tissue fraction for rapid determination of fish diet switching using stable isotope analysis

2009· article· en· W2057951639 on OpenAlexvenueno aff
M. Robbins Church, Joseph L. Ebersole, Kirk M. Rensmeyer, Ryan B. Couture, Frederic T. Barrows, David L. G. Noakes

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersU.S. Environmental Protection Agency
KeywordsMucusRainbow troutBiologyJuvenileSalmoIsotope analysisMuscle tissueHatcheryJuvenile fishZoologyAnimal scienceFisheryFish <Actinopterygii>EcologyAnatomy

Abstract

fetched live from OpenAlex

Stable isotope analysis of diet switching by fishes often is hampered by slow turnover rates of the tissues analyzed (usually muscle or fins). We examined epidermal mucus as a potentially faster turnover “tissue” that might provide a more rapid assessment of diet switching. In a controlled hatchery experiment, we switched the diet of juvenile steelhead (sea-run rainbow trout, Oncorhynchus mykiss ) from a plant-based feed with low δ 13 C and δ 15 N to a fish-meal-based diet with higher delta values. We found mucus to provide a significantly more rapid response to diet switching (half-life = 36 days for δ 15 N, 30 days for δ 13 C) than muscle tissue (half-life = 94 days for δ 15 N, 136 days for δ 13 C), even for growing juvenile fish. Mucus may provide a rapid turnover “tissue” for analysis of diet (or habitat) switching by fish. It has the additional advantage that it may be sampled nonlethally in some fishes, thereby avoiding problems in studying threatened or endangered species. This is the first report of the use of fish mucus in stable isotope analyses of fish tissues.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.257
Teacher spread0.237 · 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

Citations104
Published2009
Admission routes1
Has abstractyes

Explore more

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