Photographic evidence of tagging impacts for two beluga whales from the Cumberland Sound and western Hudson Bay populations
Bibliographic record
Abstract
Beluga whale ( Delphinapterus leucas (Pallas, 1776)) movements, habitat use, and diving behaviour have been studied using satellite-linked transmitters for decades. The inaccessibility of Arctic and subarctic habitats makes these instruments especially valuable for beluga research. The long-term effects that tags and tag attachments have on belugas, however, are not well known because resightings occur relatively infrequently. Here, we describe two belugas photographed during photographic monitoring programs of two populations: western Hudson Bay and Cumberland Sound. The beluga photographed in western Hudson Bay had scars consistent with the tag pins migrating out, which is thought to occur when the tag is pulled posteriorly due to drag. The beluga photographed in Cumberland Sound had all three tag pins still in place 11–21 years after they were inserted. Both whales appeared to be in good body condition with no evidence of infection, and the beluga from Cumberland Sound was accompanied by a 1-year-old calf. Resightings of previously tagged whales are infrequent for the western Hudson Bay population and have never been documented in Cumberland Sound. However, through long-term photographic monitoring programs, additional sightings may provide more information regarding the method of tag loss and the long-term effects of tagging on whale health and productivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".