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Record W4309152086 · doi:10.1061/9780784484432.040

Vulnerability Assessment of Portland Water System in an M9 Cascadia Subduction Zone Earthquake

2022· article· en· W4309152086 on OpenAlexaffabout
Ahmed Nisar, Ryan M. Nelson, Christopher Hitchcock, Vladimir Calugaru, Michael Greenfield

Bibliographic record

VenueLifelines 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsGreenfield Research (Canada)
Fundersnot available
KeywordsSubductionGeologyVulnerability (computing)Quarter (Canadian coin)Plan (archaeology)SeismologyPipeline transportPopulationEnvironmental scienceArchaeologyGeographyTectonicsEnvironmental engineering

Abstract

fetched live from OpenAlex

The City of Portland’s water system is the largest in the state of Oregon covering an area of approximately 225 square miles and serving almost one-quarter of the population of the state. The water system services 165 pressure zones and has over 2,000 miles of pipelines, two major dams, 38 pump stations, 59 distribution system tanks, and 10 terminal storage reservoirs. Some of the oldest components of the system are over 100 years old. In 2009 dollars, the replacement value of the system was estimated to be $6.7 billion. A comprehensive seismic study of the Portland’s water system was completed to assess its performance in an M9 earthquake on the Cascadia Subduction Zone. A long-term system improvement plan was developed to meet the stated recovery goals in the Oregon Resilience Plan, a plan developed under the direction of Oregon House of Representatives to protect lives and maintain economic activity following an M9 earthquake.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2022
Admission routes2
Has abstractyes

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