Pronounced chemical response of Subarctic lakes to climate‐driven losses in surface area
Bibliographic record
Abstract
Losses in lake area have been observed for several Arctic and Subarctic regions in recent decades, with unknown consequences for lake ecosystems. These reductions are primarily attributed to two climate-sensitive mechanisms, both of which may also cause changes in water chemistry: (i) increased imbalance of evaporation relative to inflow, whereby increased evaporation and decreased inflow act to concentrate solutes into smaller volumes; and (ii) accelerated permafrost degradation, which enhances sublacustrine drainage while simultaneously leaching previously frozen solutes into lakes. We documented changes in nutrients [total nitrogen (TN), total phosphorus (TP)] and ions (calcium, chloride, magnesium, sodium) over a 25 year interval in shrinking, stable, and expanding Subarctic lakes of the Yukon Flats, Alaska. Concentrations of all six solutes increased in shrinking lakes from 1985-1989 to 2010-2012, while simultaneously undergoing little change in stable or expanding lakes. This created a present-day pattern, much weaker or absent in the 1980s, in which shrinking lakes had higher solute concentrations than their stable or expanding counterparts. An imbalanced evaporation-to-inflow ratio (E/I) was the most likely mechanism behind such changes; all four ions, which behave semiconservatively and are prone to evapoconcentration, increased in shrinking lakes and, along with TN and TP, were positively related to isotopically derived E/I estimates. Moreover, the most conservative ion, chloride, increased >500% in shrinking lakes. Conversely, only TP concentration was related to probability of permafrost presence, being highest at intermediate probabilities. Overall, the substantial increases of nutrients (TN >200%, TP >100%) and ions (>100%) may shift shrinking lakes towards overly eutrophic or saline states, with potentially severe consequences for ecosystems of northern lakes.
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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 | 0.000 |
| 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".