Lipid corrections in carbon and nitrogen stable isotope analyses: comparison of chemical extraction and modelling methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
1. Lipids have more negative delta(13)C values relative to other major biochemical compounds in plant and animal tissues. Although variable lipid content in biological tissues alters results and conclusions of delta(13)C analyses in aquatic food web and migration studies, no standard correction protocol exists. 2. We compared chemical extraction and mathematical correction methods for freshwater and marine fishes and aquatic invertebrates to better understand impacts of correction approaches on carbon (delta(13)C) and nitrogen (delta(15)N) stable isotope data. 3. Fish and aquatic invertebrate tissue delta(13)C values increased significantly following extraction for almost all species and tissue types relative to nonextracted samples. In contrast, delta(15)N was affected for muscle and whole body samples from only a few freshwater and marine species and had a limited effect for the entire data set. 4. Lipid normalization models, using C : N as a proxy for lipid content, predicted lipid-corrected delta(13)C for paired data sets more closely with parameters specific to the tissue type and species to which they were applied. 5. We present species- and tissue-specific models based on bulk C : N as a reliable alternative to chemical extraction corrections. By analysing a subset of samples before and after lipid extraction, models can be applied to the species and tissues of interest that will improve estimates of dietary sources using stable isotopes.
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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 | 0.000 | 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 it