Estimating the configuration of the subglacial drainage system under a mountain glacier in St. Elias Mountains, Yukon
Bibliographic record
Abstract
Numerous studies have documented that water at the ice-bed interface can affect ice flow dynamics of both, mountain glaciers and the Greenland ice sheet. Water at the bed is routed through a complex network of conduits that form a subglacial drainage system. The subglacial drainage system evolves over the melt season in response to the changes in the meltwater supply. However, it is challenging to study due to the inaccessibility of the glacier bed. We use an extensive near-bed water pressure data set from an ablation zone of a small, polythermal, mountain glacier in St. Elias Mountains, Yukon. Pressure sensors, that exhibit common diurnal variations, are considered to be connected to a hydraulically active drainage system. We use a simplified two-dimensional continuum version of the subglacial drainage model with an additional assumption that changes in drainage configuration are negligible over a short time period. Spatially varying permeability function is used as a proxy for the subglacial drainage configuration, assuming that the areas of high (low) permeability correspond to the areas that are connected (disconnected) to a hydraulically active system. In order to study the evolution of the subglacial drainage system over the melt season, we divide the melt season in a series of short time periods. We then use the inverse model to estimate the permeability function for each of these time periods. Continuity is ensured by using, respectively, the final pressure distribution and the estimated permeability function of the previous period, as the initial condition and the a priori estimate for the consequent time period.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".