Utility of Pop-Up Satellite Archival Tags to Study the Summer Dispersal and Habitat Occupancy of Dolly Varden in Arctic Alaska
Bibliographic record
Abstract
In Arctic Alaska, Dolly Varden Salvelinus malma is highly valued as a subsistence fish; however, little is known about its marine ecology. New advances in electronic tagging, such as pop-up satellite archival tags (PSATs), provide scientists with a fishery-independent means of studying several aspects of this species’ movement and ecology. To evaluate the usefulness of this technology, we attached 52 PSATs to Dolly Varden in the Wulik River, which flows from northwestern Alaska into the Chukchi Sea, to study several characteristics of the marine habits of this species. Overall, PSATs provided unprecedented information about summer dispersal of Dolly Varden, including the first evidence of offshore dispersal in the Chukchi Sea, as well as previously documented dispersal types such as movement to other rivers and southerly nearshore movements in northwestern Alaska. On the basis of minimal observable evidence of tag-induced behavioral effects, as well as movements of more than 450 km by fish at liberty (i.e., between tag deployment and release or recapture), we conclude that PSATs offer an effective alternative method for studying several aspects of Dolly Varden dispersal and ecology in areas where it is not practical or feasible to capture these fish, such as coastal and offshore regions of Arctic Alaska
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".