High Arctic lakes as sentinel ecosystems: Cascading regime shifts in climate, ice cover, and mixing
Bibliographic record
Abstract
Climate and cryospheric observations have shown that the high Arctic has experienced several decades of rapid environmental change, with warming rates well above the global average. In this study, we address the hypothesis that this climatic warming affects deep, ice‐covered lakes in the region by causing abrupt, threshold‐dependent shifts rather than slow, continuous responses. Synthetic aperture radar (SAR) data show that lakes (one freshwater and four permanently stratified) on Ellesmere Island at the far northern coastline of Canada have experienced significant reductions in summer ice cover over the last decade. The stratified lakes were characterized by strong biogeochemical gradients, yet temperature and salinity profiles of their upper water columns (5‐20 m) indicated recent mixing, consistent with loss of their perennial ice and exposure to wind. Although subject to six decades of warming at a rate of 0.5°C decade−1, these lakes were largely unaffected until a regime shift in air temperature in the 1980s and 1990s, when warming crossed a critical threshold forcing the loss of ice cover. This transition from perennial to annual ice cover caused another regime shift whereby previously stable upper water columns were subjected to mixing. Far northern lakes are responding discontinuously to climate‐driven change via a cascade of regime shifts and have an indicator value beyond the regional scale.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".