Telemetry tags increase the costs of swimming in northern fur seals, <i>Callorhinus ursinus</i>
Bibliographic record
Abstract
Abstract Animal‐borne instruments have become a standard tool for collecting important data from marine mammals. However, few studies have examined whether placement of these data loggers affects the behavior and energetics of individual animals, potentially leading to biasing data. We measured the effect of two types of relatively small data loggers (<1% of animals’ mass and front profile) on the swimming speeds and energy expenditure of four female northern fur seals ( Callorhinus ursinus ) while swimming at depth. Swim speeds and rates of oxygen consumption were measured as the trained fur seals repeatedly swam an underwater circuit, with or without the tags. We found the placement of either tested tag significantly affected both the behavior and energetics of the fur seals in our study. Diving metabolic rate increased an average of 8.1%–12.3% (depending on tag type) and swim speed decreased an average of 3.0%–6.0% when wearing the tags. The combined changes in velocities and metabolic rates resulted in a 12.0%–19.0% increase in the total energy required by the fur seals to swim a set distance. The demonstrated effects of tags on behavior and energy expenditure may bias data sets from wild animals and potentially incur longer‐term impacts on the studied animals.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.006 |
| 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 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".