Linking surface hydrology to flow regimes and patterns of velocity variability on Devon Ice Cap, Nunavut
Bibliographic record
Abstract
Abstract Supraglacial meltwater reaching a glacier bed can increase ice surface velocities via hydraulic jacking and enhanced basal sliding. However, the relationships between the structure of supraglacial drainage systems, sink-point distributions, glacier flow processes and the magnitude of interannual velocity variability are poorly understood. To explore the hypothesis that spatial variations in the rate and mechanisms of glacier flow are linked to variations in supraglacial drainage system structure and sink-point distribution across an ice cap, we mapped supraglacial drainage systems on Devon Ice Cap from Landsat-7 ETM+ imagery. Spatial patterns of surface velocity and interannual velocity variability were determined using gradient correlation applied to Landsat-7 ETM+ images. Velocity variability is greater in areas close to sink-point locations, presumably because hydrologically forced basal sliding and/or bed deformation are enhanced in such areas. The distribution and characteristics of supraglacial drainage systems may play an important role in determining the distribution of regions of basal sliding, highlighting the need for knowledge of the supraglacial drainage system structure and sink-point distribution to inform efforts to model the dynamic response of Arctic ice caps to future climate warming.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".