Solubilization of skin collagen improves the accuracy and reliability of stable isotope measurements
Bibliographic record
Abstract
Stable isotope analysis of skin collagen is useful for detecting short-term or seasonal diet. Preparation of skin for stable isotope analysis varies across laboratories, and this may impact the comparability of data. It is important to understand the effects of different preparatory protocols on the stable isotopic and elemental compositions of skin samples. Using a Eurovector 3,300 elemental analyzer coupled to a Nu Horizon isotope ratio mass spectrometer, we tested the impact of three treatment variants (refluxing at three temperatures to remove non-collagenous proteins, treatment with sodium hydroxide (NaOH), and chemical lipid extraction using 2:1 chloroform:methanol) on the stable isotope (δ13carbon (C) and δ15nitrogen (N)) and elemental (wt% C, and wt% N) composition of pig (Sus scrofa domesticus) skin. The refluxing step produced pig skin with higher δ13C values, lower C:NAtomic ratios, less variable C:NAtomic ratios, wt% C, and wt% N. The chemical lipid extraction also produced higher, more reliable δ13C values and lower, less variable C:NAtomic ratios. The isotopic data in the lipid-extracted and refluxed samples were more consistent in the refluxed samples than the non-refluxed and non-lipid-extracted samples, as determined by the elemental compositions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".