Positive Correlation between DYFI Intensity Data and Microzonation Site Classes for Ottawa, Quebec City, and the Metropolitan Area of Montreal
Bibliographic record
Abstract
Abstract At the local scale, seismic risk is often poorly estimated when considering equal hazard values across any given community. Indeed, past damaging earthquakes have shown that site conditions, which may amplify or deamplify ground shaking, have an influence on the spatial distribution of damage in urban areas. In eastern Canada, Leda clay deposits from the old Champlain Sea are of particular concern for strong site effects in many parts of Quebec and Ontario. To capture the variability in seismic site response, microzonation maps characterizing average shear wave velocity for the upper 30 m of soil, and predominant resonance frequency have been developed for Montreal, Ottawa, and Quebec City. The maps derived from seismic and borehole measurements have been used to develop shake map scenarios but have not been validated, because there have not been any significantly large, close earthquakes in recent years, and because the seismograph network coverage is not adequate to provide a detailed picture of variations in shaking across a city. Nevertheless, all the three cities are in or near active seismic zones, and felt reports, although less accurate than instrumental data, are numerous and provide a dense dataset showing relative shaking levels across a region. Using intensity data for several moderate earthquakes collected largely via the Canadian internet “Did You Feel It?” page, we systematically compare reported shaking levels to soil conditions indicated by the microzonation maps. This study shows a clear correlation between high-reported intensities and soft soils for Montreal where the number of observations is the largest. The results suggest that intensity data collected via the internet and social media could provide a viable method for validating microzonation maps and shaking scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".