Glacial erosion: controls and global distribution
Bibliographic record
Abstract
Glacial erosion has often been parameterized as proportional to glacier sliding velocity, while the role played by local geology, hydrology and climate remain largely unquantified. As a result, our understanding of the links between global climate, tectonics and glacial erosion is limited. To address this shortcoming, we present a comprehensive synthesis of previously published Quaternary glacial erosion rates from six different measurement techniques integrated over 10-2 to 106 years: (i) instrumental measurements beneath active glaciers, (ii) sediment fluxes derived from meltwater streams or (iii) ice-marginal deposits, (iv) terrestrial cosmogenic nuclide dating (TCN), (vi) luminescence thermochronometry, and (v) relief generation of chronologically constrained surfaces. Our synthesis includes 1065 empirical data points and 465 erosion rates from ice sheets, ice caps, and topographically confined glaciers that range over six orders of magnitude, between 10-4 and 100 mm yr-1. Using a filtered dataset of contemporary erosion rates, we apply machine learning tools, using available environmental, glaciological, and geological datasets to assess the dominant controls on subgroups of nominal data categories. On a global scale, while glacial sliding velocity is an important control, we also discover equally strong or stronger correlations between other glaciological, environmental, and lithological parameters and glacial erosion rate, some of which have not been previously documented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".