Meltwater percolation, impermeable layer formation and runoff buffering on Devon Ice Cap, Canada
Bibliographic record
Abstract
Abstract The retention of meltwater in the accumulation area of the Greenland ice sheet and other Arctic ice masses buffers their contribution to sea level change. However, sustained warming also results in impermeable ice layers or ‘ice slabs’ that seal the underlying pore space. Here, we use a 1-D, physically based, high-resolution model to simulate the surface mass balance (SMB), percolation, refreezing, ice layer formation and runoff from across the high-elevation area of Devon Ice Cap, Canada, from 2001 to 2016. We vary the thickness of the ‘impermeable’ ice layer at which underlying firn becomes inaccessible to meltwater. Thick near-surface ice layers are established by an initial deep percolation, the formation of decimetre ice layers and the infilling of interleaving pore space. The cumulative SMB increases by 48% by varying impermeable layer thickness between 0.01 and 5 m. Within this range we identify narrower range (0.25–1 m) that can simulate both the temporal variability in SMB and the observed near-surface density structure. Across this range, cumulative SMB variation is limited to 6% and 45–49% of mass retention takes place within the annually replenished snowpack. Our results indicate cooler summers after intense mid-2000s warming have led to a partial replenishment of pore space.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".