Evaluation of muscle lipid extraction and non‐lethal fin tissue use for carbon, nitrogen, and sulfur isotope analyses in adult salmonids
Bibliographic record
Abstract
Rationale Chemical lipid extraction or using alternative tissues such as fish fin as opposed to muscle may alter isotopic ratios and influence interpretations of δ 13 C, δ 15 N, and previously unassessed δ 34 S values in stable isotope analyses (SIA). Our objectives were to determine if lipid extraction alters these isotope ratios in muscle, if lipid normalization models can be used for lipid‐rich salmonids, and if fin isotope ratios are comparable with those of muscle in adult salmonids. Methods In six adult salmonid species (n = 106) collected from Lake Ontario, we compared three isotope ratios in lipid‐extracted (LE) muscle with bulk muscle, and LE muscle with fin tissue, with paired t‐tests and linear regressions. We compared differences between δ 13 C values in LE and bulk muscle with predicted values from lipid normalization models and the log‐linear model of best fit and determined model efficiency. Results The δ 15 N values in LE muscle increased (<1‰) relative to bulk muscle for most salmonids, with relationships nearing 1:1. There were either no differences or strong 1:1 relationships in δ 34 S values between species‐specific bulk and LE muscle. One lipid normalization model had greater model efficiency (97%) than the model of best fit (94%). Fin had higher δ 13 C values than LE muscle while δ 15 N trends varied (<1‰); however, both isotope ratios had either no or weak linear relationships with fin and LE muscle within species. The δ 34 S values in fin were similar to those in LE muscle and had strong 1:1 relationships across species. Conclusions We recommend using the lipid normalization model to adjust for δ 13 C values in lipid‐rich muscle (C:N >3.4). LE muscle could be used without δ 15 N or δ 34 S adjustments, but the minimal increase in δ 15 N values may affect SIA interpretation. With high unexplained variability among adult species in fin‐muscle δ 13 C and δ 15 N relationships, species‐specific fin‐muscle adjustments are warranted. No fin‐muscle tissue adjustment would be required for δ 34 S values.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".