DETERMINING BLUBBER FATTY ACID COMPOSITION: A COMPARISON OF <i>IN SITU</i> DIRECT AND TRADITIONAL METHODS
Bibliographic record
Abstract
A bstract 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".