Extreme warming and regime shift toward amplified variability in a far northern lake
Bibliographic record
Abstract
Abstract Mean annual air temperatures in the High Arctic are rising rapidly, with extreme warming events becoming increasingly common. Little is known, however, about the consequences of such events on the ice‐capped lakes that occur abundantly across this region. Here, we compared 2 years of high‐frequency monitoring data in Ward Hunt Lake in the Canadian High Arctic. One of the years included a period of anomalously warm conditions that allowed us to address the question of how loss of multi‐year ice cover affects the limnological properties of polar lakes. A mooring installed at the deepest point of the lake (9.7 m) recorded temperature, oxygen, chlorophyll a (Chl a) fluorescence, and underwater irradiance from July 2016 to July 2018, and an automated camera documented changes in ice cover. The complete loss of ice cover in summer 2016 resulted in full wind exposure and complete mixing of the water column. This mixing caused ventilation of lake water heat to the atmosphere and 4°C lower water temperatures than under ice‐covered conditions. There were also high values of Chl a fluorescence, elevated turbidity levels and large oxygen fluctuations throughout fall and winter. During the subsequent summer, the lake retained its ice cover and the water column remained stratified, with lower Chl a fluorescence and anoxic bottom waters. Extreme warming events are likely to shift polar lakes that were formerly capped by continuous thick ice to a regime of irregular ice loss and unstable limnological conditions that vary greatly from year to year.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".