Urea and lipid extraction treatment effects on δ <sup>15</sup> N and δ <sup>13</sup> C values in pelagic sharks
Bibliographic record
Abstract
RATIONALE: Stable isotope analysis (SIA) provides a powerful tool to investigate diverse ecological questions for marine species, but standardized values are required for comparative assessments. For elasmobranchs, their unique osmoregulatory strategy involves retention of (15)N-depleted urea in body tissues and this may bias δ(15)N values. This may be a particular problem for large predatory species, where δ(15)N discrimination between predator and consumed prey can be small. METHODS: We evaluated three treatments (deionized water rinsing [DW], chloroform/methanol [LE] and combined chloroform/methanol and deionized water rinsing [LE+DW]) applied to white muscle tissue of 125 individuals from seven pelagic shark species to (i) assess urea and lipid effects on stable isotope values determined by IRMS and (ii) investigate mathematical normalization of these values. RESULTS: For all species examined, the δ(15)N values and C:N ratios increased significantly following all three treatments, identifying that urea removal is required prior to SIA of pelagic sharks. The more marked change in δ(15)N values following DW (1.3 ± 0.4‰) and LE+DW (1.2 ± 0.6‰) than following LE alone (0.7 ± 0.4‰) indicated that water rinsing was more effective at removing urea. The DW and LE+DW treatments lowered the %N values, resulting in an increase in C:N ratios from the unexpected low values of <2.6 in bulk samples to ~3.1 ± 0.1, the expected value of protein. The δ(13)C values of all species also increased significantly following LE and LE+DW treatments. CONCLUSIONS: Given the mean change in δ(15)N(1.2 ± 0.6‰) and δ(13)C values (0.7 ± 0.4‰) across pelagic shark species, it is recommended that muscle tissue samples be treated with LE+DW to efficiently extract both urea and lipids to standardize isotopic values. Mathematical normalization of urea and lipid-extracted δ(15)N(LE+DW) and δ(13)C(LE+DW) values using the lipid-extracted δ(15)N(LE) and δ(13)C(LE) data were established for all pelagic shark species.
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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 | 0.000 | 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 | 0.000 |
| 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".