Drivers of global glacial erosion rates
Bibliographic record
Abstract
Glacial erosion must be quantified to better constrain numerous geomorphic and orogenic processes; however, accurate models of glacial erosion have been limited by sparse data. Here we use machine learning tools to develop equations that integrate glacial erosion and glaciological, topoclimatic and geological variables based on a global-scale synthesis of 181 contemporary glacier-derived erosion rates. The results reveal environment-specific erosion rate equations for surge-type, marine- and land-terminating glacial settings. We demonstrate that glacial velocity is not the most statistically important predictor of glacial erosion in any environment. Instead, an improved prediction of glacial erosion is attained when velocity is considered with additional glaciological, topoclimatic and geological variables, with the most dominant influences exhibited by precipitation, glacial elevation, length, latitude and the underlying geology. Using these equations, we estimate erosion rates for 85% of contemporary glaciers, with 99% eroding between 0.02 and 2.68 mm yr−1. Our results suggest a need to adjust how we predict or hindcast glacial erosion rates and highlight their sensitivity not only to changes in glacial sliding velocity but also to additional glaciological, topoclimatic and geological influences. Considering glaciological, topoclimatic and geological variables in addition to glacial velocity improves the prediction of glacial erosion rates according to a machine learning-based global analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".