Climatic controls on frost cracking and implications for the evolution of bedrock landscapes
Bibliographic record
Abstract
In mountainous landscapes the role of periglacial processes in producing sediment is poorly defined, despite evidence of abundant talus slopes. Ice growth in rock has long been recognized as an efficient erosion mechanism, but the effects have not been readily applied to landscape evolution in response to tectonic and climatic forcing. Here, we quantify how and where ice‐driven mechanical erosion occurs in cold, bedrock‐dominated landscapes using a simple one‐dimensional numerical heat flow model. In our model, ice grows by water migration to colder regions in shallow rock by the reduction in chemical potential associated with intermolecular forces between ice and mineral surfaces, a process called segregation ice growth. The depth and intensity of frost cracking is primarily dependent on mean annual temperature (MAT), with positive MAT sites characterized by intense cracking in the top meter of the rock mass and a maximum frost penetration of ∼4 m. In contrast, negative MAT areas have less intense cracking that primarily occurs at depths between 50 and 800 cm. We compare the depth and intensity of frost cracking predicted by our model with measures of the intensity of frost processes determined in three studies: The first measured the timing of rockfall in the Canadian Rockies, Niagara Escarpment, and Japanese Alps; the second analyzed scree deposits in the Southern Alps, New Zealand; and the third documented rockfall frequency in Utah. These natural examples show that rockfalls tend to nucleate at elevations that coincide with zones of intense frost cracking predicted by our model. As such, climatic variations associated with interglacial‐glacial cycles may impart a significant influence on the denudation of mountainous landscapes.
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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 | 0.000 | 0.000 |
| 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".