Seasonal depth and temperature use, and diel movements of lake trout (<i>Salvelinus namaycush</i>) in a subarctic lake
Bibliographic record
Abstract
We conducted a multi-year acoustic telemetry study of lake trout (Salvelinus namaycush (Walbaum, 1792)) in a small subarctic lake to investigate depth and temperature occupancy, and vertical activity across seasons (summer, fall, and winter), diel periods (day, twilight, and night), and during summer periods of 24 h light (day and twilight). Analyses using generalized additive mixed models revealed a high degree of individual variation in depth occupancy independent of the factors hour of day, season, and diel period, whereas temperature occupancy and vertical activity were explained using the three combined factors. Habitats occupied were typically 9–20 m and 6–9.5 °C in summer, 1–3 m and 2–15 °C in fall during presumed spawning, and ≤6 m and <3 °C in winter. Lake trout exhibited partial diel migration where individuals displayed a variety of vertical migratory directions within and among seasons or diel period, including during periods of 24 h light. Fish were most vertically active during periods of daylight and in fall. During 24 h light, some lake trout performed crepuscular movements, whereas individual behaviour best explained modelled depth and temperature occupancy and vertical activity. The variety of vertical patterns among individuals and seasons suggests multifactor proximate causes of partial diel migration and crepuscular movements.
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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 | 0.000 | 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".