Correlating DYFI Data With Seismic Microzonation in the Region of Montreal
Bibliographic record
Abstract
The Western Quebec seismic zone has moderate seismic activity with few historical damaging earthquakes. Nevertheless, recent risk analyses have shown that the combination of a high level of urbanization with soft soil deposits in the metropolitan area of Montreal could lead to significant damage and economic losses. Over the two decades, several projects have been completed to develop a seismic microzonation to identify zones where seismic waves could be amplified. During the same period, Natural Resources Canada developed an internet application to collect reports from the population after an earthquake and to convert them to the Modified Mercalli Intensity scale (MMI). This paper presents a first comparison of the MMI data compiled after eight recent earthquakes felt in Montreal area with the existing zonation in terms of soil classes. It shows that the MMI from individual reports increases when the observer is located in a soft soil zone. Statistics on average MMI over a regular grid confirms this trend. The numerous reports collected through the internet application, and future applications based on data collected from social media, could become a very useful source of information to complement seismic field measurements when developing and validating seismic microzonation maps.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".