Climate warming and projected loss of thermal habitat volume in lake populations of brook trout
Bibliographic record
Abstract
We applied an ensemble of climate warming models to an iconic protected landscape (Algonquin Park, Ontario) and the seasonal temperature profile model for lakes to assess changes in brook trout ( Salvelinus fontinalis) thermal habitat volume (THV) among lakes of different sizes in 30-year periods under two climate warming scenarios (RCP 4.5 and 8.5). Bayesian beta regression models show that lake size (surface area) and morphometry (dynamic lake ratio) are important factors in THV loss. THV loss increases as a function of the dynamic lake ratio (transition from bowl-shaped to dish-shaped lakes). The magnitude of this effect depends on the lake size category and the RCP scenario. Small (<100 ha) and medium (100–500 ha) dish-shaped lakes are projected to have greater THV loss in 2071–2100 (60%–100% of brook trout THV under RCP 8.5; 40%–70% under RCP 4.5) than large lakes (>500 ha) of similar shape. Climate warming projections for the balance of this century, regardless of the RCP category, will result in the loss of brook trout THV in lakes that range widely in size and morphometry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".