Physical modelling of thaw slumps in a geotechnical centrifuge 
Bibliographic record
Abstract
Retrogressive thaw slumps are a well-known arctic geohazard, which often occur in ice-rich permafrost. Thaw slumps can be triggered by warming and/or anthropogenic influences. Consequences of thaw slumps include changes to the landscape, impacts to infrastructure, sediment and solute loads to watersheds, and release of stored carbon, among other effects. Many studies on thaw slumps in nature include external monitoring through use of time-lapse photography, unmanned aerial vehicle (UAV), and lidar surveys. While field studies using external monitoring equipment provide high quality information about the extent and consequence of thaw slumps, direct observations of thermal and mechanical mechanisms occurring behind the scarp normally remain hidden. Recent advances at Royal Military College of Canada (RMC) used physical modeling to examine cold regions phenomena with a geotechnical centrifuge. Geotechnical centrifuges apply elevated gravity to small-scale models to create stress-equivalent environments and allow for direct observation of subsurface displacements from digital images of the model's side profile. Instrumentation in thaw slump physical models includes internal temperature measurements using fiber optics and scarp face temperature measurements using a thermal camera. Preliminary results indicate that the thaw slump physical models are conceptually capturing key behaviours observed from external field measurements. Typically, warming begins at the face and surface leading to thawing and and episodic thaw slump events. Failed material migrates downward and away from the intact block. This mechanism repeats until the final slump occurs . Internal displacements, measured using digital image corellation (DIC), show corellation with co-located temperature measurements. Results also show higher ice contents and taller scarps tend to lead to shear failure while lower ice contents and shorter scarps tend to fail via a toppling mechanism. Outcomes of the research will provide a practical analysis tool for analyzing thaw slumps as well as fundamental understanding of pre-failure permafrost mechanics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".