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Record W3109414733 · doi:10.1785/0220200281

A Critical Assessment of Canadian Earthquake Monitoring and Alerting Practice versus the Initial Challenges of the 2020 COVID-19 Experience

2020· article· en· W3109414733 on OpenAlexaffabout
D. McCormack, A L Bent, Reid Van Brabant, L McKee

Bibliographic record

VenueSeismological Research Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)SeismometerResilience (materials science)Pandemic2019-20 coronavirus outbreakFunction (biology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)H1n1 pandemicComputer securityAeronauticsComputer scienceOperations researchSeismologyEngineeringMedicineGeologyVirology

Abstract

fetched live from OpenAlex

Abstract We describe the regular pre-COVID mode of operations for the Canadian National Seismograph Network and the associated monitoring, alerting, and analysis for earthquakes in Canada; we describe how the current operational posture evolved and discuss the ways in which the posture was and was not suitable to respond to the challenges and constraints of the COVID-19 situation in Canada. We find that many of the design and operation decisions that have been taken over the last several decades for earthquake monitoring in Canada, collectively driven largely by considerations of resilience and cost-effectiveness and further refined after the experience of the H1N1 pandemic, resulted in a system that continued to function effectively under lockdown conditions. There were many earthquakes in Canada that required seismologist response during the lockdown, all of which were handled remotely without issue. Specific challenges and lessons learned from the first few months of the pandemic are noted.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.299
GPT teacher head0.459
Teacher spread0.160 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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