Experimental validation confirms a carbon stable isotope lipid normalization procedure for Pacific salmon
Bibliographic record
Abstract
Carbon stable isotope analysis is an important tool in studies of fish ecology. Studies using this tool must account for the effect of lipids present in fish tissue on carbon stable isotope (δ13C) values. Simple correction equations have been developed to correct for this effect. For taxa with high fat content, choosing an accurate correction equation can have a significant impact on results. Pacific salmon (Oncorhynchus spp.) are a lipid-rich genus. Their broad marine distribution and ecological, cultural, and commercial importance make them prime candidates for stable isotope analysis. To determine both an accurate lipid correction equation for Pacific salmon δ13C values, and the effect of lipid extraction on bulk nitrogen isotope (δ15N) values, we performed pairwise isotope analysis on lipid-extracted and untreated muscle samples from 68 Chinook salmon O. tshawytscha spanning a size range of 165-750 mm and C:N values of 2.96-14.25. We compared the fit of existing δ13C lipid correction equations from 3 previously published models to our data, and optimized the top performing model using a leave-one-out cross validation. The model of Kiljunen et al. (2006; https://doi.org/10.1111/j.1365-2664.2006.01224.x) performed the best (mean squared error: 0.22, r2: 0.91), while the optimized model only slightly improved on it (MSE: 0.20, r2: 0.93). For δ15N, we determined that Chinook δ15N values significantly increased by 0.6‰ following lipid extraction. Our results confirm a lipid normalization procedure that is broadly applicable to Pacific salmon, and supports streamlined analysis of δ13C and δ15N from a single untreated muscle tissue sample.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".