Habitat overlap of juvenile and adult lake trout of Great Bear Lake: Evidence for lack of a predation gradient?
Bibliographic record
Abstract
Abstract A range of organisms, from plankton to fish, commonly shift their habitat distributions horizontally or vertically due to predation risk. Juvenile lake trout,Salvelinus namaycush, are generally viewed as occupying deep areas of lakes to decrease predation pressure from adults. In contrast, we found that juvenile lake trout from Great Bear Lake, NT, Canada, occupied a variety of habitats and from shallow to deep depths (0–150 m), overlapping with adult lake trout. No evidence occurred for a length depth‐based segregation (e.g., ontogenetic shift). Genetic variation was also similar among juveniles in the different depth zones. However, isotopic niches and C:N ratios among juveniles showed some variability in niche widths and positions for individuals caught from the 51–150 m zone compared to juvenile individuals caught from 0–20 m and 21–50 m zones. The uniformly distributed adult lake trout in Great Bear Lake may evenly distribute predation pressure (including cannibalism) across shallow‐ and deep‐water habitats more than in other lakes. As a result, juveniles may respond to differences in foraging opportunities rather than predation risks. Juvenile lake trout did not appear to conform to the general pattern of juveniles seeking a deep‐water refuge to reduce predation risks. In contrast, juvenile lake trout of Great Bear Lake displayed broad resource use across all depths and habitats.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".