Depth and temperature selection of lake charr (Salvelinus namaycush) ecotypes in Lake Superior revealed by popup satellite archival tags
Bibliographic record
Abstract
Lake charr exhibit morphological diversity in large North American lakes, largely attributed to habitat partitioning. Bathythermal habitats of lean lake charr have been assessed but remain largely unknown for other lake charr ecotypes. Popup satellite archival tags (PSATs) were used to determine depth and temperature profiles of lean (n = 15), siscowet (n = 16) and redfin (n = 3) lake charr ecotypes in Lake Superior during segments of the year. Monthly median depths of leans were < 20 m throughout the year while the median depth of redfins was 24–32 m (June-September). Monthly median depth of siscowets ranged from 103 to 204 m but they exhibited four distinctive patterns: deep (>80 m), shallow (<10 m), extreme vertical movements (>80–0 m), and high frequency vertical movements between 100 and 125 m. Siscowets were the most stenothermal with habitat temperatures generally 4–5 °C but not exceeding 12 °C. Leans were found as low as 0 °C (January-March) and up to 15 °C (July-September). Median monthly habitat temperatures for redfins were 1–3 °C colder than leans during July-September. PSATs confirmed that siscowets exhibit extreme vertical movements and may feed near the surface. The timing and duration of the extreme vertical movements exhibited by siscowets did not appear to be associated with time of day (i.e., not diel vertical movements) and may be described as opportunistic movements presumably related to foraging. Data obtained from the PSATs reinforces the view that lake charr are highly adaptive and can thrive in cold, oligotrophic lakes by optimizing the use of the entire water column.
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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 | 0.000 | 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) | 0.001 | 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".