Fin‐icky samples: an assessment of shark fin as a source material for stable isotope analysis
Bibliographic record
Abstract
Analyzing stable isotopes (SI: δ15N and δ13C) in a new tissue requires rigorous testing before its general application in examining aspects of animal ecology. Shark fin provides a novel, minor invasive source material, which is important considering the conservation status of many large sharks. Fin, however, is not a single tissue but composed of multiple tissues, primarily skin and cartilage. This may complicate the interpretation of SI, as fin can be sampled from multiple fins and different regions of a fin from an individual. Here, we examined the variation in δ15N and δ13C with sample location on the anal fin of Caribbean reef sharks (Carcharhinus perezi). Values of δ15N and δ13C were highly correlated across sampling locations indicating that mean population or size class fin SI data would be reliable. At the individual level, large variation in δ15N and δ13C between anal fin sampling locations indicates that the varying proportional contributions of tissues would complicate individual level analyses. For three pelagic shark species, dorsal fin δ13C values were consistently higher than δ13C muscle tissue values, identifying tissue‐specific diet discrimination factors. This would confound multiple tissue studies that assume that SI values across tissues will be equal if the animal is in equilibrium with its diet. Proposed sampling protocols for fin material will negate many of these issues, but caution is warranted for comparisons of SI data between shark fin and other tissues or across species until the isotope dynamics of fin have been experimentally validated.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".