Deciphering Freeze-Thaw Dynamics in Rockwalls: A Novel Approach for high accuracy Regional-scale Modelling.
Bibliographic record
Abstract
Using a large and novel array of instruments on five rockwalls in northern Gaspesia, their respective surface energy balances were calculated and their thermal regime were measured and modeled to depths exceeding the seasonal frost penetration. A parametric analysis of the thermal properties and structural characteristics of the instrumented rockwalls was then performed. The roles of solar radiation exposure, surface thermal absorptivity, lithology and weathering degree on the distribution of sporadic freeze-thaw cycles and on the seasonal frost distribution over a winter were quantified. The fine spatiotemporal scale of our measurements and models revealed complex thermal configurations in the first meters of rockwalls, including frozen layers sandwiched between thawed ones and vice versa. Freeze-thaw cycle frequency was primarily driven by solar radiation exposure and surface absorptivity, while seasonal frost penetration was strongly influenced by lithology and weathering. The parametric analysis based on thermal and structural properties representative of the study area enabled us to extrapolate a local thermal regime model to a regional scale without needing to instrument as many sites as there is diversity in exposure, absorptivity, lithology and degree of weathering. Other parameters, such as slope inclination, snow accumulation, and climate warming, can also be tested with this approach. This hybrid method, which combines field measurements and modelling, is intended to quantify the thermal regime of multiple rockwalls more accurately than spatial modelling and climate reanalyzes, while drastically reducing the effort, cost and risk of conventional instrumentation. It could represent a valuable tool for regional hazard management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".