Rock wall freeze-thaw dynamic in a climate change context
Bibliographic record
Abstract
Meteorological conditions control the impact of mechanical and chemical weathering on rock wall erosion and contribute strongly to rock wall instability. The predominant role of frost weathering on rock wall erosion has been demonstrated. Three kinds of freeze-thaw cycles are commonly defined and related to rockfall frequency and magnitude: daily, seasonal and perennial (permafrost). However, the spatiotemporal distribution of freeze-thaw cycles at a fine spatial scale remains poorly studied. This study aims to further define freeze-thaw cycles, to model the freeze-thaw dynamic and to explore the respective influence of solar radiation exposure, thermal absorptivity, lithology, degree of weathering as well as climate change on freeze-thaw dynamic at a fine spatial scale. Four thermistor sensors were inserted in horizontal boreholes 3 to 5.5 meter deep in different north and south facing rock wall structure with changing geology from massive conglomerate to highly stratified and fractured sedimentary rocks. These measurements, combined with weather conditions recorded locally, provide information on the freeze-thaw dynamic and allow to validate thermodynamic simulations run with the WUFI® software. Results show that there is a diversity of daily freeze-thaw cycles and that they can interact with the seasonal freezing front. For a north exposed rock wall, the change in solar radiation exposure to the south-west leads to 247% more surface freeze-thaw events but to a 20% decrease of these cycles at 50 cm depth. The change in lithology or in degree of weathering of the first meters of rock mainly influences the maximum depth of the freezing front, the depth of winter thaw events, as well as the rate of spring thaw. The freeze-thaw front modeled in this study reached an average depth of 420 cm for the period 1980-2009 against 208 and 111 cm respectively for the period 2070-2099 with the RCP4.5 and RCP8.5 scenarios. For these same periods, the duration of the seasonal freeze-thaw front decreases respectively from 149 days to 102 and 36 days according to the RCP4.5 and RCP8.5 scenarios. This study demonstrates the great sensitivity of freeze-thaw dynamic to the modification of topographic or climatic variables. Spatial change as rock wall exposure or temporal change as climate warming have significant repercussions on the effectiveness of frost weathering both on the periods of occurrence and on the depths concerned. Moreover, daily and seasonal freeze-thaw cycles are complex phenomena and because of their frequent interactions, they cannot be analyzed separately.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".