Connection between thermal stress and frost quakes
Bibliographic record
Abstract
Global warming is affecting the Arctic more significantly as it is warming faster than other places on Earth. The consequences for Arctic as well as sub-Arctic environment are not well understood. Observations in the past decades and climate change impact analysis predicts clear changes in snow cover and snow melt but consequences to frozen soil and related phenomena such as frost quakes are unclear. Frost quakes are non-tectonic seismic events that occur when freezing of water in saturated soils or rocks results in sudden release of seismic energy. Compared to traditional tectonic earthquakes in seismology, frost quakes are much less studied, as they usually occur at random, or less predictable, rarely instrumented locations. Reports and news of frost quakes, resulting in mechanical damage to the pavements, roads and buildings have been received recently from different locations in Finland, Canada and USA and connections between air temperature and frost quakes have been found. The conceptual model of frost quakes is well known but a methodology to predict the occurrence of frost quakes have been missing. In our study, we present a methodology to investigate the connection between thermal stress and frost quakes. Thermal stress is a function of temperature, which can be measured or calculated. We used a hydrological model to calculate snow depth, snow melt rate and soil temperature at different depths in soil. We show that rapid decrease in temperature can cause a thermal stress that is higher than fracture toughness and strength of the soil‐ice mixture. A swarm of frost quakes occurred on 6 January 2016, in in the city of Oulu in Central Finland (sub-Arctic environment). Some of the frost quakes created ruptures in soil, building foundations, and roads. We show that origin of frost quakes was related to rapid decrease in air temperature from -12 °C to –29 °C that created thermal stress in frozen soil and roads which could not withstand the stress.
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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 | 0.000 |
| 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".