Decades of supraglacial hydrological network evolution on Ellesmere Island’s glaciers
Bibliographic record
Abstract
Abstract Over the past two decades, the Canadian Arctic Archipelago has undergone significant glacier mass loss, driven primarily by surface melt. This study presents a detailed analysis of supraglacial drainage evolution along Ellesmere Island’s ∼830 km latitudinal extent using satellite imagery, historical aerial photographs and DEMs from 1959 to 2020. Analysis of five glaciers shows that drainage density ( D d ) has increased over time, driven by the expansion of perennial rivers, especially at higher elevations. Far northern glaciers exhibit stable, well-developed drainage systems, while southern glaciers show a relatively greater increase in canyon development since 1959. Cold surface ice in the north supports higher D d , while southern glaciers with extensive sinks (moulins and large crevasses) exhibit stronger surface-to-bed connectivity. Despite increased channelization, sinuosity changes remain statistically insignificant, reflecting dynamic canyon behavior governed by surface slope and meltwater discharge. Results align with modeled increases in melt, especially on southern glaciers where supraglacial systems have expanded most rapidly. Continued equilibrium line altitude rise under future warming is expected to intensify melt and result in the expansion of supraglacial drainage systems up-glacier, particularly for glaciers with large amounts of ice at mid-elevation.
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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 | 0.000 | 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) | 0.001 | 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".