Strong motion seismograph networks, data, and research in Canada
Bibliographic record
Abstract
Strong motion monitoring has undergone a revolution in Canada in recent years. Most analogue,non-communicating instruments have been replaced with modern digital instruments that provideinformation in real-time. Dense networks are being deployed in the urban centres of southwest BritishColumbia to provide shaking parameters and "shake maps" immediately after an earthquake.Monitoring of critical infrastructure, including bridges, dam sites and transmission facilities is increasing. This article documents the current state of strong motion monitoring across Canada, andsummarises the data sets that are currently available. As of 2008, the Geological Survey of Canada operates 110 strong motion instruments (Internet Acclerometers or IA's) across Canada, most of whichare deployed in the urban centres of high seismic hazard in southwest British Columbia. Partnerorganisations operate an additional 70 strong motion instruments monitoring critical infrastructure inwestern Canada. In eastern Canada, the GSC operates a network of 18 strong motion instruments inthe active Charlevoix zone, and 6 IA's in greater Ottawa. Partner organisations operate instruments atan additional 15 sites. During the past decade, more than 700three component accelerograms have been recorded across Canada. While some large ground motions have been recorded (peak groundacceleration (PGA) greater than 2g), most of the records represent weak motion (PGA less than 5%g).These are useful for evaluating local site response, which in turn will be valuable to engineersevaluating strong ground shaking during future earthquakes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".