Near-surface permeability in a supraglacial drainage basin on the Llewellyn Glacier, Juneau Icefield, British Columbia
Bibliographic record
Abstract
Abstract. Supraglacial channel networks link time varying melt production and meltwater routing on temperate glaciers. Such channel networks often include components of both surface transport in streams and subsurface porous flow through near-surface ice, firn or snowpack. Although subsurface transport if present will likely control network transport efficacy, it is the most poorly characterized component of the system. We present measurements of supraglacial channel spacing and network properties on the Juneau Icefield, subsurface water table height, and time variation of hydraulic characteristics including diurnal variability in water temperature. We combine these data with modeling of porous flow in weathered ice to infer near-surface permeability. Estimates are based on an observed phase lag between diurnal water temperature variations and discharge, and independently on measurement of water table surface elevation away from a stream. Both methods predict ice permeability on a 1–10 m scale in the range of 10−10–10−11 m2. These estimates are considerably smaller than common parameterizations of surface water flow on bare ice in the literature, as well as smaller than most estimates of snowpack permeability. For supraglacial environments in which porosity/permeability creation in the subsurface is balanced by porous flow of meltwater, our methods provide an estimate of microscale hydraulic properties from observations of supraglacial channel spacing.
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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 | 0.001 | 0.001 |
| 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".