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Record W2326640548 · doi:10.1061/41016(314)35

Seismic Monitoring of British Columbia Bridges

2008· article· en· W2326640548 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueStructures Congress 2008 · 2008
Typearticle
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsMinistry of HealthMinistry of Transportation of Ontario
Fundersnot available
KeywordsEarthquake scenarioUrban seismic riskSoftware deploymentChristian ministrySeismic riskCrippleForensic engineeringEmergency managementEmergency responseEnvironmental planningSeismic hazardCivil engineeringSeismologyGeologyEngineeringEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

The west coast of BC lies in Canada's highest seismic zone under threat of three different types of large, highly destructive earthquakes. The British Columbia Ministry of Transportation (BCMoT) is responsible for 400 km of provincial Disaster Response Routes. The loss of any portion of one of these routes could significantly impact emergency response efforts and negatively affect public well being. The Ministry maintains 900 structures in the highest seismic zones, many of which are vulnerable to extensive damage in even a moderate quake and potential collapse in a major earthquake. The loss of the use of several structures would not only have immediate impact on public well being and the ability of emergency vehicles to respond effectively, but would also cripple the economic recovery of the region. The effects would be felt across the nation and for many years into the future. The better the information on which areas, structures and facilities are most vulnerable, the better planning and preparation can be done. By identifying those structures and facilities most susceptible to seismic forces, decision-makers can do effective risk management. Fast, accurate field intelligence immediately following an earthquake can ensure the most effective deployment of vital services and mitigate damage to the built environment. Preparation and mitigation will both aid economic recovery. Earthquakes hazards are very complex and each event unique. Most of the significant advances in identifying and understanding seismic hazards over the past 50 years have been aided by the availability of seismic monitoring data. The BCMoT has in the past had few bridges instrumented. Five structures have between two and six accelerometers and two structures have limited strain gauges. Only one structure uploads data to an internet site. The data from all the other structures must be collected at the site. This has proven most inefficient. The BCMoT has embarked on a program to instrument key structures to provide confirmation of seismic capacity, assist in focussing retrofit efforts, detect damage from any cause and provide rapid damage assessment of those structures following a seismic event. The data from this instrumentation will be capable of remote configuration and will automatically upload via the internet. The Ministry is working with the University of British Columbia to develop effective damage detection algorithms that will provide reliable intelligence close to real time. The purpose of this paper is to evaluate the status and effectiveness of the implementation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.716

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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