Update on the Canadian National Seismograph Network
Bibliographic record
Abstract
<!--!introduction!--> Natural Resources Canada (NRCan) completed the refurbishment of the Canadian National Seismograph Network in 2019 and has since started commissioning an Earthquake Early Warning System, due to be completed in 2024. We give an overview of these projects and summarize the status of the networks. Meanwhile, the software, databases and procedures used for location and alerting of earthquakes in Canada are being modernized. NRCan is deprecating AutoDRM and Antelope in favour of an FDSNWS and SeisComP respectively. Custom legacy database schema, file formats, and automatic location and prompt earthquake notification software are being replaced with QuakeLink, StationXML, QuakeML and a commercial software called GDS. The new system will consist of two fully redundant datacentres, with configurations continuously regression tested to ensure continuous improvement. Earthquake solutions from neighbouring agencies will be available to analysts in real time. Some of the SeisComP plugins developed for NRCAN will be of use to other network operators, including new location and magnitude estimators as well as tools for the efficient generation of waveform data availability statistics. Adoption of QuakeML has forced us to develop a clear event type hierarchy and interpret evaluation status meaningfully. NRCan will mark the transition to SeisComP by adopting a new agency code. We report the progress and discuss lessons learned from this process. Finally, we report on the development of a new system to monitor volcanoes using InSAR, as well as coda envelope moment magnitudes and a regional seismic travel time model for 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.061 |
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".