Is lipid correction necessary in the stable isotope analysis of fish tissues?
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Bibliographic record
Abstract
RATIONALE: Stable isotope analysis (SIA) is a powerful tool for examining diet and food-web dynamics. SIA assumes "you are what you eat" relative to carbon (C) and nitrogen (N). However, fractionation of carbon during lipid synthesis violates this assumption; high-lipid tissues do not reflect δ(13) C values of diet and therefore have the potential to skew mixing model results and diet interpretations, making corrections necessary. METHODS: Brook Trout (Salvelinus fontinalis) white muscle and liver samples from several fish species representing the temperate North American cold- and warm-water fish community were corrected for lipids via chemical lipid extraction and mathematical lipid normalization. To assess the accuracy of model-predicted lipid-free δ(13) C values calculated from four normalization models, we compared model-predicted values with those measured after lipid extraction. RESULTS: We found that chemical lipid extraction is unnecessary for Brook Trout white muscle tissue with low initial lipid content. However, in tissues with C:N ratios greater than 3.5, lipid extraction increased δ(13) C values in fish liver by more than 1.0 ‰, indicating that liver lipid content is sufficient to bias δ(13) C values. We also found that lipids were accurately accounted for with mathematical normalization and recommend that tissues with C:N ratios greater than 3.5 be corrected mathematically. CONCLUSIONS: Our findings indicate that mathematical normalization is sufficient to account for bias in δ(13) C values associated with lipid content in fish tissues when C:N ratios are above 3.5. C:N ratios below 3.5 indicate that tissues have insufficient levels of lipid to bias the δ(13) C values. Generally, these findings support the use of more timely and cost-effective processing and analysis methods in future aquatic food-web studies utilizing SIA. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it