Investigation of suspected Holocene fault scarp near Montréal, Québec: The first paleoseismic trench in eastern Canada
Bibliographic record
Abstract
Québec has experienced historical damaging earthquakes in several seismic zones (e.g. 1732 M5.8 Montréal, 1663 M7 Charlevoix, 1935 M6.2 Témiscamingue). Despite a high seismicity rate, no surface-rupturing faults have been discovered due to a combination of dense vegetation cover, recent glaciation, sparse earthquake records, and low regional strain rates. We manually searched lidar-derived digital elevation models (DEMs) of the region to search for potential post-glacial surface-rupturing faults across southern Québec and identified a scarp $\sim$50km north of Montréal. We performed three geophysical surveys (ground penetrating radar, depth estimates from ambient seismic noise, and refraction seismology) that revealed a buried scarp, confirmed with a <1 m-deep hand-dug test pit. These observations convinced us to excavate the first paleoseismic trench in Québec to test for the presence of a surface-rupturing fault in July 2023. We found a glacial diamict containing no signs of syn- or post-glacial deformation. In this paper, we present the observations that led to the identification of a scarp and hypothesized faulting. We highlight the importance of trenching to confirm recent fault scarps in challenging environments. We hope our study can be used to optimize future paleoseismic research in the province of Québec and similar intracratonic glaciated landscapes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".