Effect of seasonal changes in upwelling activity on the foraging locations of a wide-ranging central-place forager, the New Zealand fur seal
Bibliographic record
Abstract
Lactating New Zealand fur seals ( Arctocephalus forsteri (Lesson, 1828)) that breed at Cape Gantheaume, South Australia, experience broad-scale seasonal changes in ocean productivity. To assess how seasonal changes in ocean productivity influenced foraging behaviour, 18 lactating New Zealand fur seals were fitted with satellite transmitters and time–depth recorders (TDRs). Using temperature and depth data from TDRs, we used the presence of thermoclines as a surrogate measure of upwelling activity in continental-shelf waters. During the austral autumn 80% of lactating fur seals foraged on the continental shelf (114 ± 44 km from the colony), in a region associated with the Bonney upwelling. In contrast, during winter months seals predominantly foraged in oceanic waters (62%), in a region associated with the Subtropical Front (460 ± 138 km from the colony). Our results indicate that lactating New Zealand fur seals shift their foraging location from continental-shelf to oceanic waters in response to a seasonal decline in productivity over the continental shelf, attributed to the cessation of the Bonney upwelling. This study identified two regions used by lactating New Zealand fur seals: (1) a nearby and seasonally productive upwelling system and (2) a distant and permanent oceanic front.
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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 | 0.001 |
| 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) | 0.001 | 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".