Vulnerability of shallow subarctic lakes to evaporate and desiccate when snowmelt runoff is low
Bibliographic record
Abstract
Abstract Snowmelt is a crucial source of water for many shallow subarctic lakes, but climate models predict that snowfall will decrease in some regions, with profound ecological consequences. Here we use lake water isotope data across gradients of terrestrial vegetation cover (open tundra to closed forest) and topographic relief to identify lakes that are vulnerable to desiccation under conditions of low snowmelt runoff in two subarctic landscapes—Old Crow Flats, Yukon, and Hudson Bay Lowlands, Manitoba (Canada). Lakes located in low‐relief, open tundra catchments in both landscapes displayed a systematic, positive offset between directly measured lake water δ18O over multiple sampling campaigns and lake water δ18O inferred from cellulose in recently deposited surface sediments. We attribute this offset to a strong evaporative 18O‐enrichment response to lower‐than‐average snowmelt runoff in recent years. Notably, some lakes underwent near‐complete desiccation during midsummer 2010 following a winter of very low snowfall. Based on the paleolimnological record of one such lake, the extremely dry conditions in 2010 may be unprecedented in the past ~200 years. Findings fuel concerns that a decrease in snowmelt runoff will lead to widespread desiccation of shallow lakes in these landscapes.
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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 | 0.001 |
| 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".