Variable uptake and elimination of stable nitrogen isotopes between tissues in fish
Bibliographic record
Abstract
We conducted a diet-switching experiment using freshwater ocellate river stingrays (Potamotrygon motoro) fed a novel earthworm (Eisenia foetida) diet to establish the relative contributions of growth and metabolism to δ 15 N values in an elasmobranch species. We specifically controlled for the potential effects of protein composition of experimental diets on δ 15 N turnover to determine whether δ 15 N turnover after a low to high δ 15 N diet switch (uptake) and a high to low δ 15 N diet switch (elimination) will occur at the same rate within each consumer tissue. Our results showed that the turnover of δ 15 N from metabolism and growth differed between uptake and elimination phases in the liver, blood, cartilage, and muscle of freshwater stingrays. During uptake, liver was found to track dietary δ 15 N more closely than the other tissues, with the highest metabolic turnover rate of δ 15 N (0.015 day 1 ), whereas cartilage had the slowest rate of metabolic δ 15 N turnover (0.0022 day 1 ) relative to a constant rate of growth among tissues (0.003 day 1 ). We propose that estimates of trophic position from muscle sampling alone have considerable uncertainty, particularly for scavenging or omnivorous species. We suggest that multitissue sampling can identify this problem and lead to a more robust evaluation of trophic dynamics for individual species.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".