The Silence of Canadian Cities: The Seismology Impact of the Covid19 Lockdown
Bibliographic record
Abstract
On 11 March 2020, the World Health Organization (WHO) declared Coronavirus disease (COVID-19) as a pandemic. The announcement had a cascading effect as countries around the world rushed to declare various states of emergencies. Canada was no exception. All Canadian provinces and territories implemented some health emergency measures to check the spread of COVID-19. This provided an opportunity to study the changes in seismic vibrations registered by the land-based seismic stations before, during, and after the lockdown. I analyzed continuous seismic data for 6 Canadian cities: Calgary (Alberta), Edmonton (Alberta), Montreal (Quebec), Ottawa (Ontario), Toronto (Ontario), and Yellowknife (Northwest Territories). These cities represent the wide geographical spread of Canada. The source of data for the study was seismic stations run by the Canadian National Seismograph Network (CNSN). Data available freely on the Incorporated Research Institutions for Seismology (IRIS) website was used. Python and ObsPy were used to load and convert raw data into Probabilistic Power Spectral Densities (PPSDs). The seismic vibrations in the PPSDs that fell between 0.1 HZ and 20 HZ were extracted and averaged for every two weeks period to determine the trend of seismic vibrations. The lockdown had an impact on seismic vibrations in almost all the cities I analyzed. Except for Ottawa, the seismic vibrations decreased between 14% - 44% with the biggest decrease in Yellowknife in the Northwest Territories. In the 3 densely packed cities of the population over 1 million - Toronto, Montreal, and Calgary, the seismic vibrations dropped by over 30%. In the case of Ottawa, the seismic vibrations increased by 8%. As not all seismic stations were equally close to the cities, they were not equally sensitive to changes in human activities. Furthermore, while lockdown happened in all the cities selected for the study, the strictness enforced and the participation of people in the lockdown varied. Many cities extended the lockdown without any change while others extended the lockdown with a loosening of restrictions. All these differences induced variations in the study. Finally, a comprehensive online training module was created using Jupyter notebooks to allow researchers to analyse lockdown data from other seismic stations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".