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Record W2981968645 · doi:10.1785/0220190202

The Canadian National Seismograph Network: Upgrade and Status

2019· article· en· W2981968645 on OpenAlexaffabout
A L Bent, Timothy J. Côté, H. Seywerd, D. McCormack, Kathryn A. Coyle

Bibliographic record

VenueSeismological Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSeismometerUpgradeBroadbandInstrumentation (computer programming)SeismologyTelecommunicationsGeographyComputer scienceEngineeringEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Abstract The Canadian National Seismograph Network (CNSN) operated by Natural Resources Canada consists of approximately 200 stations. Data from this network are used to produce the national earthquake catalog, to provide alerts in the aftermath of an earthquake, to develop the national seismic hazard maps, and for research within Canada and internationally. A significant upgrade to the instrumentation and infrastructure of the CNSN, which began in 2014, is nearing completion. The newly refurbished network is uniform in terms of instrumentation with the remaining single-component short-period stations converted to three-component broadband stations and consistent sampling rates across the network. Strong-motion instruments are now collocated with weak-motion instruments at many sites in all regions of Canada, and there is also a significant increase in the number of stand-alone strong-motion sites. Improvements in telecommunications were aimed at improving reliability and decreasing latency. All upgraded stations undergo a series of quality checks before the data are approved for dissemination. Data from the CNSN are freely available to the seismological community and the general public.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.294
Teacher spread0.238 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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