Mucus: a new tissue fraction for rapid determination of fish diet switching using stable isotope analysis
Bibliographic record
Abstract
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 δ13C and δ15N 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 δ15N, 30 days for δ13C) than muscle tissue (half-life = 94 days for δ15N, 136 days for δ13C), 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".