An Analysis of Past and Future Changes in the Ice Cover of Two High-Arctic Lakes Based on Synthetic Aperture Radar (SAR) and Landsat Imagery
Bibliographic record
Abstract
Space-borne remotely sensed data can provide valuable insight into cryospheric processes in remote high-latitude regions for which direct observations are limited. In this study we use synthetic aperture radar (SAR) and Landsat imagery to evaluate recent changes in the ice cover of Upper and Lower Murray Lakes (81°20′N, 69°30′W) on Ellesmere Island, Nunavut, Canada. These data highlight changes in ice conditions that have occurred over the past decade and provide a means for assessing the likely impacts of rising temperatures on future lake-ice conditions. Under current (1997–2007) climatic conditions the Murray Lakes average several weeks of ice-free conditions in August and early September, although in some years a partial ice cover persists throughout the year. The observed relationship between summer temperature and ice melt at Upper and Lower Murray Lakes suggests that recent warming in the High Arctic has forced the lakes near a threshold from a state characterized by perennial ice cover to the current state that includes seasonal melting of lake ice. Projected future warming will significantly increase the duration of ice free conditions on Upper and Lower Murray Lakes, with ice-out predicted to occur 13.5 ± 4.0 and 17.6 ± 5.6 days earlier, respectively, for every 1 °C increase in mean June–July temperature.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".