Distinct freshwater migratory pathways in Arctic char (<i>Salvelinus alpinus</i>) coincide with separate patterns of marine spatial habitat-use across a large coastal landscape
Bibliographic record
Abstract
Understanding variability in distributions and habitat-use among populations of anadromous salmonids is essential for their sustainable management. Arctic char ( Salvelinus alpinus) is an important cultural and socioeconomic species; however, knowledge of their spatiotemporal habitat-use during the marine phase is limited. Here, a large-scale acoustic telemetry array was used to determine intraspecific variation in Arctic char summer marine habitat-use tied to overwintering lake occurrence in the Amundsen Gulf. Arctic char tagged in the ocean migrated to two main overwintering lakes, corresponding to distinct migration corridors and separate patterns of marine habitat-use, with one individual exhibiting among the longest recorded char marine migration to date (∼330 km). Arctic char that undertook longer migration distances initiated travel in the ocean towards fresh water 11 days earlier than those completing shorter migration distances; mean departure days (±SD) 2 August (±8.1 days) and 13 August (±6.8 days), corresponding to migration distances of 252 and 131 km, respectively. These findings identify that Arctic char from different populations can occupy distinct marine foraging grounds within a region, with consequences for variable interactions with fisheries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".