Assessment of wound healing of tagged gray (<i>Eschrichtius robustus</i>) and blue (<i>Balaenoptera musculus</i>) whales in the eastern North Pacific using long‐term series of photographs
Bibliographic record
Abstract
Abstract Tags have been used to examine migration routes and habitat use of large whales for >40 yr, however, evaluation of tag wound healing has largely been short‐term, anecdotal or generalized. This study developed methods for systematic photographic assessment of long‐term external consequences of tag placement, to determine potential differences in wound healing between species and tag types and thus advise future tagging efforts to possibly minimize undesirable side effects. Tag site appearance and healing characteristics were evaluated by two reviewers and a time series evaluated by five veterinarians from photographs during 995 postdeployment encounters with 34 gray and 63 blue whales tagged in the North Pacific. Blue whale resightings were less frequent, but spanned a longer time period due to earlier tag deployments than the more frequent gray whale follow‐up observations. Swelling occurred in 74% of reencountered gray whales, with the highest frequency 6 mo postdeployment. Swellings were common in blue whales with early tag designs but rare with current models. Depressions occurred in 82% of gray and 71% of blue whales. This study demonstrates the value of follow‐up studies of tagged animals and systematic scoring of photographs to quantitatively compare tag response.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".