Summer habitat use and feeding of juvenile Arctic charr, <i>Salvelinus alpinus</i>, in the Canadian High Arctic
Bibliographic record
Abstract
Abstract – Owing to limited knowledge of the habitat use and diet of juvenile Arctic charr from the High Arctic, particularly young‐of‐the‐year (YOY), we assembled data obtained from samples taken in and around Lake Hazen, Nunavut, Canada, to assess juvenile habitat use and feeding. Juvenile charr demonstrated a preference for stream environments, particularly those fed by warm upstream ponds. Charr occupying both stream and nearshore lake habitats were found to feed similarly, with chironomids occurring most frequently in diets. Some older stream‐dwelling charr preyed on smaller, younger Arctic charr. Preferred stream occupancy is likely mediated by physical barriers created mainly by water velocity, and by distance from the lake, lake‐ice dynamics, low water depth, and turbidity. Water velocities resulted in stream habitat segregation by size, with YOY mainly found in low‐velocity pools and back eddies adjacent to stream banks, but not in water velocities >0.1 m·s−1. Greatest charr densities in streams were found in small, shallow, slow‐flowing side channels, which are highly susceptible to drought. Under predicted climate change scenarios, streams fed by small ponds will be susceptible to intermittent flow conditions, which could result in increased competition among juvenile charr for the remaining stream habitats. In addition, glacier‐fed streams are likely to experience increased flow conditions that will exacerbate physical barriers created by water velocity and further reduce the availability of preferred stream habitat.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".