Foraging a new trail with northern fur seals (<i>Callorhinus ursinus</i>): Lactating seals from islands with contrasting population dynamics have different foraging strategies, and forage at scales previously unrecognized by <scp>GPS</scp> interpolated dive data
Bibliographic record
Abstract
Abstract We reconstructed the foraging tracks of lactating northern fur seals ( Callorhinus ursinus ) from two eastern Bering Sea islands (St. Paul Island and Bogoslof Island) using linear interpolation between GPS locations recorded at a maximum of four times per hour and compared it to tri‐axial accelerometer and magnetometer data collected at 16 Hz to reconstruct pseudotracks between the GPS fixes. The high‐resolution data revealed distances swum per foraging trip were much greater than the distances calculated using linearly interpolated GPS tracks (1.5 times further for St. Paul fur seals and 1.9 times further for Bogoslof fur seals). First passage time metrics calculated from the high resolution data revealed that the optimal scale at which the seals searched for prey was 500 m (radius of circle searched) for fur seals from St. Paul Island that went off‐shelf, and 50 m for fur seals from Bogoslof Island and surprisingly, 50 m for fur seals from St. Paul that foraged on‐shelf. These area‐restricted search scales were significantly smaller than those calculated from GPS data alone (12 km for St. Paul and 6 km for Bogoslof) indicating that higher resolution movement data can reveal novel information about foraging behaviors that have important ecological implications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".