Stable isotope signatures of whisker and blood serum confirm foraging strategies for female New Zealand sea lions (<i>Phocarctos</i> <i>hookeri</i>) derived from telemetry
Bibliographic record
Abstract
Recognizing the individual variability of foraging behaviour of marine predators is important for understanding their role in the marine ecosystem and identifying how species may respond to environmental variability or human impacts. This research examines stable isotope signatures (δ13C and δ15N) of blood serum and whiskers from 22 female New Zealand sea lions (Phocarctos hookeri (Gray, 1844)) to determine if the isotopic composition of serum reflects foraging strategy, and whether serum and proximal whisker growth have similar signatures, therefore indicating the isotopic composition of whiskers also reflects the foraging strategy diet at the time of their growth. Female New Zealand sea lions are known to have two distinct foraging strategies (mesopelagic or benthic ecotypes), shown to be habitual within and between years. Females who are known to be mesopelagic foragers have higher overlap and are at greater risk of harmful interactions with fisheries. This research found that the two foraging strategies identified from telemetry are also associated with different δ13C and δ15N isotopic values from blood serum and whiskers. Therefore, stable isotope analysis could be used to determine the proportion of the female population that are likely to be exposed to the detrimental direct and indirect interactions with fisheries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".