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Record W2103326938 · doi:10.4095/215097

The new strong motion seismic network in southwest British Columbia, Canada

2004· report· en· W2103326938 on OpenAlexaffabout
A. Rosenberger, K. I. Beverley, Garry C. Rogers

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSeismologyGeologyMotion (physics)GeographyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The Geological Survey of Canada has designed a new type of low-cost strong motion seismometer and is currently updating its strong motion seismic network with the new instruments. As of January 2004 about fifty instruments are in operation in Southwestern British Columbia and in a dense urban demonstration network in the cities of Vancouver and Richmond. With a noise floor of 0.5 mg (with g beeing the earth's acceleration) and a range of ±4 g over a frequency band 0 - 42 Hz the instrument is ideally suited for urban strong motion networks. The instrument can also be fitted with external velocity sensors for use in studies of building structural dynamics. Continuous full waveform data are recorded and stored in an internal ring-buffer by each instrument. Data can be retrieved with standard Internet protocols like FTP and SSH/SCP at any time. Parametric data such as peak ground acceleration (PGA), velocity (PGV) and spectral intensity (PSI) from an event as measured by an individual instrument are reported in near real time and are used to generate an experimental shake-map as a tool for emergency response agencies. The instruments employ solid-state sensors and are virtually maintenance free. Instrument network configuration and acquisition parameters can be managed remotely over the Internet. The techniques to maintain continuous Internet connections with the instruments have proven to be robust and reliable.

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)0.000
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: none
Teacher disagreement score0.533
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.190
Teacher spread0.180 · 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

Citations6
Published2004
Admission routes2
Has abstractyes

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