Bibliographic record
Abstract
Frost cracking is a dominant mechanical weathering phenomenon facilitating the breakdown of bedrock in periglacial regions. Despite recent advances in understanding frost cracking processes, few studies have addressed how global climate change over the Late Cenozoic may have impacted spatial variations in frost cracking intensity. In this study, we estimate global changes in frost cracking intensity (FCI) by segregation ice growth. Existing process-based models of FCI are applied in combination with soil thickness data from the Harmonized World Soil Database. Temporal and spatial variations in FCI are predicted using surface temperatures changes obtained from ECHAM5 general circulation model simulations conducted for four different paleoclimate time-slices. Time-slices considered include Pre-Industrial (~1850 CE, PI), Mid-Holocene (~6 ka, MH), Last Glacial Maximum (~21 ka, LGM) and Pliocene (~3 Ma, PLIO) times. Results indicate for all paleoclimate time slices that frost cracking was most prevalent (relative to PI times) in the mid to high latitude regions, as well as high-elevation lower latitudes areas such the Himalayas, Tibet, European Alps, the Japanese Alps, the USA Rocky Mountains, and the Andes Mountains. The smallest deviations in frost cracking (relative to PI conditions) were observed in the MH simulation, which yielded slightly higher FCI values in most of the areas. In contrast, larger deviations were observed in the simulations of the colder climate (LGM) and warmer climate (PLIO). Our results indicate that the impact of climate change on frost cracking was most severe during the PI – LGM period due to higher differences in temperatures and glaciation at higher latitudes. In contrast, the PLIO results indicate low FCI in the Andes and higher values of FCI in Greenland and Canada due to the diminished extent of glaciation in the warmer PLIO climate.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.010 |
| Insufficient payload (model declined to judge) | 0.143 | 0.150 |
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".