Variability in milk fatty acids: recreating a foraging trip to test dietary predictions in Antarctic fur seals
Bibliographic record
Abstract
Using Antarctic fur seals (Arctocephalus gazella (Peters, 1875)) in a feeding trial, we investigated the use of milk fatty acids to determine diet. In a regime designed to replicate an average foraging trip, six female seals were fed on four successive days meals of krill (Euphausia superba Dana, 1850; day 1), mixed krill and icefish (Champsocephalus gunnari Lönnberg, 1905; day 2), and icefish (days 3 and 4). Four milk samples were collected from each female, one every 12 h from the time of the last feed. Fatty acid profiles of samples were analysed using canonical discriminant analysis and classification trees. Milk fatty acids could be used to distinguish between seals fed the experimental diet, which was high in fish, and those feeding naturally, thought to be consuming mostly krill. However, there was significant variation between individual seals, which may have been the influence of feeding prior to the experiment. Milk fatty acids remained relatively unchanged over time, suggesting that the influence of dietary fatty acids was averaged over the suckling period. We conclude that, whilst this technique has potential in studying the diet of higher predators, further work is needed to test the underlying assumptions and mechanisms involved in the transfer of fatty acids from the diet into milk.
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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 | 0.001 |
| 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 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".