Stable isotope analysis: modelling lipid normalization for muscle and eggs from arctic mammals and birds
Bibliographic record
Abstract
Summary 1. Lipids are more depleted in 13 C than proteins. Variable lipid contents in tissues affect therefore the measurements of stable carbon isotope ratios. Model based (also called mathematical) normalization has been suggested to correct δ 13 C values using the ratio of carbon to nitrogen (C/N) as a proxy for lipid content. This approach has not been thoroughly validated for terrestrial animals and it is not clear to what extent it is generally applicable or species/tissue specific. 2. Ratios of stable carbon isotopes (δ 13 C) were obtained for muscle samples of 22 mainly terrestrial arctic mammal and bird species and for egg samples of 32 bird species from nine sites in the circumpolar Arctic. We used linear and nonlinear equations to model the difference in δ 13 C between samples from which lipids had been extracted chemically and bulk tissue samples. Models were compared on the basis of a model selection criterion (AIC) and of prediction error as estimated by cross‐validation. 3. For muscle samples, a linear and a nonlinear equation performed equally well. The observed values were also well predicted by a previously published general equation for aquatic organisms. For egg samples, a nonlinear equation fitted separately to waterfowl and non waterfowl bird species fitted the data best. Prediction errors were, however, larger than for muscle samples. 4. The generality of the inferred normalization equations was assessed by applying them to a second data set from a similar ecosystem, but produced in the frame of another study. The predicted lean δ 13 C values were within 0·5‰ of the observed values for 73% of the muscle samples, but only for 27% of the egg samples. 5. Based on our results, we recommend model based normalization of δ 13 C values as an economic way to deal with varying lipid contents in muscle samples of mammals and birds. For egg samples, on the contrary, model based predictions had large errors. Therefore, we recommend chemical lipid extraction in order to estimate lipid‐free δ 13 C values for egg content.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".