Are long subglacial bedforms indicative of fast ice flow?
Bibliographic record
Abstract
It has been suggested that extremely long subglacial bedforms (e.g. attenuated drumlins and mega‐scale glacial lineations) record former areas of fast‐flowing ice and that bedform elongation ratio is a useful proxy for ice velocity. Despite the availability of much data pertaining to the measurement and analysis of subglacial bed‐forms, these assumptions have rarely been explicitly addressed in detail. In this paper, we demonstrate that long subglacial bedforms (length:width ratios 10:1) are indicative of fast ice flow. Using satellite imagery, we mapped over 8000 lineaments associated with a highly convergent flow pattern near Dubawnt Lake, District of Keewatin, Canada. This flow pattern is unusual in that it displays a large zone of convergence feeding into a main ‘trunk’ and then diverging towards the inferred ice margin. The ‘bottleneck’ pattern is taken to record an increase and subsequent decrease in ice velocity and we analysed transverse and longitudinal variations in bedform morphometry. The main trunk of the flow pattern (down‐ice of the convergent zone) is characterized by mega‐scale glacial lineations of great length (up to 13 km) and high elongation ratios (up to 43:1). The down‐ice variations in elongation ratio reflect exactly what we would expect from a terrestrial ice stream whose velocity increases in the onset zone passes through a maximum in the main trunk and slows down as the ice diverges at the terminus. It is suggested that any unifying theory of drumlin formation must be able to account for the association between long subglacial bedforms and fast ice flow, although it is not assumed that fast ice flow always produces attenuated bedforms. A further implication of this work is that many more ice streams may be identified on the basis of attenuated subglacial bedforms, radically altering our views on the flow dynamics of former ice sheets.
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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 | 0.001 |
| 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".