DETERMINING BLUBBER FATTY ACID COMPOSITION: A COMPARISON OF <i>IN SITU</i> DIRECT AND TRADITIONAL METHODS
Bibliographic record
Abstract
Abstract Fatty acids (FAs) are used to make inferences about the foraging behavior and diets of free‐ranging marine mammals. However, several methods are currently available for determining the FA composition of blubber and these methods may produce different results. We compared in situ direct transesterification methods, where a small amount of tissue is sampled, with more traditional methods involving prior lipid extraction of the entire sample of interest. Using gray seal (Halichoerus grypus) and beluga whale (Delphinapterus leucas) blubber, we found that when the direct in situ method was used on a 2‐mg sample of blubber, the resulting FA profile differed significantly from that produced when traditional full‐extraction methods were employed. Regardless of where the small spot sample was taken within the blubber depth, it was not representative of the entire blubber FA composition, as blubber is non‐homogeneous throughout its depth. We also modified the in situ direct method to allow analysis of the entire blubber layer. Results of this full‐layer direct method compared quite favorably with traditional extraction methods and may provide a reasonable alternative for analyses. Although application of our full‐layer direct method will require further verification in certain marine mammal blubber samples, we conclude that the large differences obtained when using the direct method are not a consequence of the chemical method itself. Rather, they arise from non‐representative sampling of the blubber FA composition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".