COLLECTION, ASSESSMENT AND REPORTING OF MACROSEISMIC INTENSITY IN CANADA
Bibliographic record
Abstract
Macroseismic intensity measurements are a fundamental part of seismic event reporting as they provide a severity-based representation of ground shaking. Additionally, inclusion of macroseismic intensity measurements through “Did You Feel It” (DYFI) reports dramatically increases the density of available ground-shaking measurements. Natural Resources Canada (NRCan) has been collecting DYFI data since the 2000s and produces reports for roughly 50 felt seismic events per year. NRCan is working towards incorporating DYFI data from the United States Geological Survey for near-border events. In the past, intensity data has been primarily disseminated in map form, but aggregated reports are now available in JSON format, via the Earthquakes Canada website. As part of the upcoming National Earthquake Early Warning (EEW) System, NRCan will begin to send intensity-based alerts to the public to warn of incoming strong ground motions. NRCan is also expanding its use of reported intensity data to create ShakeMaps to assist with post-earthquake response. In this paper, we will describe NRCan’s current macroseismic intensity data collection methods and identify current and future use cases. Specifically, we will focus on the aggregation methods and applicability of intensity-prediction models in various regions of Canada.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".