Correlation of acoustic emissions with patterns of movement in an extremely slow-moving landslide at Peace River, Alberta, Canada
Bibliographic record
Abstract
The Peace River region, Alberta, Canada, has experienced extensive landslide activity since deglaciation. Shear zones within weak lacustrine silt and clay layers typically experience continuous creep, damaging highway and utilities infrastructure. However, occasionally, movement accelerates and potentially catastrophic failures occur. Conventional deformation monitoring approaches provide incremental measurements with low temporal resolution and do not necessarily allow rapid changes in stability to be detected and communicated sufficiently in advance to provide early warning. The study objectives were to (i) acquire a long-term dataset of continuous deformation measurements with high temporal resolution of a case study slope in Peace River, (ii) enhance understanding of a typical creeping Peace River slope’s behavior in response to climatic drivers, and (iii) investigate the potential of an acoustic emission (AE) monitoring system to provide early warning of accelerating deformation behavior. ShapeAccelArray (SAA) and AE instruments were installed, in addition to conventional inclinometers and piezometers. Measurements show that the landslide is “extremely slow”, moving on average 5 mm annually, and reveal seasonal activity with periods of acceleration and deceleration driven by pore-water pressures. Measured AE correlated strongly with the rate and magnitude of SAA-measured displacement, demonstrating the potential of the AE technique to warn of accelerating deformation behavior.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".