Thermokarst lakes formed in buried glacier ice: Observations from Bylot Island, eastern Canadian Arctic
Bibliographic record
Abstract
Abstract. In formerly glaciated permafrost regions, extensive areas are still underlain by a considerable amount of glacier ice buried by glacigenic sediments. Although the extent and volume of undisturbed relict glacier ice are unknown, these ice bodies are predicted to melt with climate warming but their impact on landscape evolution remains poorly studied. The spatial distribution of buried glacier ice can play a significant role in reshaping periglacial landscapes, in particular thermokarst aquatic systems. This study focuses on lake initiation and development in response to the melting of buried glacier ice on Bylot Island, Nunavut. We studied a lake-rich area using lake-sediment cores, detailed bathymetric data, remotely sensed data and observations of buried glacier ice exposures. Our results suggest that initiation of deeper thermokarst lakes was triggered by the melting of buried glacier ice. They have subsequently enlarged through thermal and mechanical shoreline erosion, as well as vertically through thaw consolidation and subsidence, and they later coalesced with neighbouring water bodies to form larger lakes. Thus, these lakes now evolve as “classic” thermokarst lakes that expand in area and volume as a result of the melting of intrasedimental ground ice in the surrounding material and the underlying glaciofluvial and till material. It is expected that the deepening of thaw bulbs (taliks) and the enlargement of Arctic lakes in response to global warming will reach undisturbed buried glacier ice, if any, which in turn will substantially alter lake bathymetry, geochemistry and greenhouse gas emissions from Arctic lowlands.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".