The impact of surface melt rate and catchment characteristics on Greenland Ice Sheet moulin inputs
Bibliographic record
Abstract
The supraglacial drainage system of the Greenland Ice Sheet, in combination with surface melt rate, controls the rate of water flow into moulins, a major driver of subglacial water pressure. We apply the Subaerial Drainage System (SaDS) model, a physically based surface meltwater flow model, to a ∼20×27km2 catchment on the southwestern Greenland Ice Sheet for 4 years of melt forcing (2011, 2012, 2015, and 2016) to (1) examine the relationship between surface melt rate and the rate, diurnal amplitude, and timing of surface inputs to moulins; (2) compare SaDS to contemporary models; and (3) present a framework for selecting appropriate supraglacial drainage models for different modelling objectives. We find that variations in the rate and timing of modelled moulin inputs related to the development of supraglacial channels are relatively more important in years with low melt volumes than years with high melt volumes. We suggest that a process-resolving supraglacial hydrology model (e.g., SaDS) should be considered when modelling outcomes are sensitive to subdiurnal and long-term seasonal changes in the rate of discharge into moulins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".