Tissue-specific isotope trophic discrimination factors and turnover rates in a marine elasmobranch: empirical and modeling results
Bibliographic record
Abstract
There are very few studies reporting isotopic trophic discrimination factors and turnover rates for marine elasmobranchs. A controlled laboratory experiment was conducted to estimate carbon and nitrogen isotope trophic discrimination factors and isotope turnover rates for blood, liver, muscle, cartilage tissue, and fin samples of neonate to young-of-the-year leopard sharks ( Triakis semifasciata ). Trophic discrimination factors varied (0.13‰–1.98‰ for δ 13 C and 1.08‰–1.76‰ for δ 15 N). Tissues reached or were close to isotopic equilibrium to the new diet after about a threefold biomass gain and 192 days. Liver and blood exhibited faster isotope turnover than muscle, cartilage tissue, and fin samples, and carbon isotopes turned over faster than those of nitrogen. Metabolic turnover contributed substantially to isotopic turnover, which differs from most reports for young marine teleosts. We modeled the relationship between muscle turnover rates and shark size by coupling laboratory results with growth rate estimates for natural populations. Model predictions for small, medium, and large wild leopard sharks indicate the time to isotopic equilibrium is from one to several years.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".