Climate buffering effects of western Canadian boreal lakes: the effect of lake size and depth on shoreline and nearshore forests
Bibliographic record
Abstract
Lakes can provide thermal refugia effects by buffering shoreline and inland temperatures, potentially delaying forest transitions. However, this effect has not been quantified for the majority of boreal Canada lakes, which are often excluded in general circulation model predictions of climate, thus potentially underestimating the effects of lake-mediated buffering. Here, we quantify the effects of varying lake morphometry on temperature buffering potential of 11 boreal lakes in central to western Canada. We aim to provide context for lake-mediated climate buffering in Canada’s boreal forest. We established inland transects at 11 lakes in Ontario, Manitoba, Saskatchewan, Alberta, and the NWT of Canada, with temperature stations at 10 m, 100 m, 1 km, 10 km, and 100 km from shore. We predicted the effects of lake characteristics on mean July temperature anomaly, net ice-off period temperature anomaly, and the proportion of coniferous trees at sites. July temperatures were coolest on the downwind side of lakes, within 10 km of shore, and at lakes with a high volume (R 2 c = 0.71), Near-shore sites were cooler than inland sites, particularly at a lower altitude above the lake and larger lake volumes (R 2 c = 0.66). Ice-off temperature anomalies were best predicted by the interaction between lake area and average lake depth (R 2 c = 0.55). Lastly, the proportion of coniferous trees at sites was best predicted by mean July temperature (R 2 c = 0.41). We identified lakes across boreal Canada large enough to provide seasonal temperature buffering on their shoreline and nearshore forests, with an aim for inclusion in circulation models and to guide management and conservation efforts associated with lake-mediated climate refugia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".