Variations in Hard‐Bedded Topography Beneath Glaciers
Bibliographic record
Abstract
Abstract The morphology of glacier beds is a first‐order control on their slip speeds and consequent rates of subglacial erosion. As such, constraining the range of bed shapes expected beneath glaciers will improve estimates of glacier slip speeds. To estimate the variability of subglacial bed morphology, we construct 10 high‐resolution (10 cm) digital elevation models of proglacial areas near current glacier margins from point clouds produced through a combination of terrestrial laser scanning and photogrammetry techniques. The proglacial areas are located in the Swiss Alps and the Canadian Rockies and consist of predominantly debris‐free bedrock of variable lithology (igneous, sedimentary, and metamorphic). We measure eight different spatial parameters intended to describe bed morphologies generated beneath glaciers. Using probability density functions, Bhattacharyya coefficients, principal component analysis, and Bayesian statistical models we investigate the significance of these spatial parameters. We find that the parameters span similar ranges, but the means and standard deviations of the parameter probability density functions are commonly distinct. These results indicate that glacier flow over bedrock may lead to a convergence toward a common bed morphology. However, distinct properties associated with each location prevent morphologies from being uniform.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".