Modelling intra-annual dynamics of a major marine-terminating Arctic glacier
Bibliographic record
Abstract
ABSTRACT Significant intra-annual variability in flow rates of tidewater-terminating Arctic glaciers has been observed in recent years. These changes may result from oceanic and/or atmospheric forcing through (1) perturbations at the terminus, such as enhanced submarine melt and changes in sea-ice buttressing, or (2) increased surface melt, in response to atmospheric warming, reaching the bed and promoting glacier slip. We examine the influence of these processes on Belcher Glacier, a large fast-flowing tidewater outlet of the Devon Island ice cap in the Canadian Arctic. A hydrologically-coupled higher-order ice flow model is used to estimate changes in glacier flow speed as a result of changes in sea-ice buttressing and hydrologically-driven melt-season dynamics. Daily run-off from five sub-catchments over the 2008 and 2009 melt seasons provides meltwater forcing for the model simulations. Model results are compared with remotely-sensed and in situ ice-surface velocity measurements. Sea-ice effects are found to have a minor influence on glacier flow speed relative to that of meltwater drainage, which is clearly implicated in short-term velocity variations during the melt season. We find that threshold drainage is essential in determining the timing of these short-lived accelerations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".