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Record W3120613124 · doi:10.1002/essoar.10506434.2

The Silence of Canadian Cities: The Seismology Impact of the Covid19 Lockdown

2021· article· en· W3120613124 on OpenAlex
Artash Nath

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSeismometerSeismologySeismic noiseGeographyAftershockPopulationSeismic zoneGeologyInduced seismicityDemography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.010
GPT teacher head0.203
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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