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Record W4309780271 · doi:10.1016/j.dib.2022.108762

Datasets of disrupted transportation networks on Canada's West Coast in a plausible M9.0 Cascadia Subduction Zone earthquake scenario

2022· article· en· W4309780271 on OpenAlexafffundabout
Luana Souza Almeida, Lauryne Rodrigues, Floris Goerlandt, Jose Ancona-Segovia, Ronald Pelot, David Bristow, Stephanie E. Chang

Bibliographic record

VenueData in Brief · 2022
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of British ColumbiaUniversity of VictoriaDalhousie University
FundersMarine Environmental Observation Prediction and Response Network
KeywordsSubductionContext (archaeology)Natural disasterResilience (materials science)PopulationGeographyGovernment (linguistics)SeismologyGeologyEnvironmental resource managementComputer scienceEnvironmental scienceMeteorologyArchaeology

Abstract

fetched live from OpenAlex

This article presents a database with geographical and demographic information characterizing the impacts to road and maritime networks, and coastal communities, of a plausible magnitude M9.0 megathrust Cascadia Subduction Zone earthquake scenario near Vancouver Island in British Columbia, Canada. The database consists of a medium and a high impact case associated with the earthquake scenario. The data include the geographical location of communities, ports, and airports/helipads/heliports, the structure of the roads network and their expected damage levels, the resilience level and population size of the communities on Vancouver Island, and the trajectories, expected delays and capacities of ferries and barges. The data originates from government and carriers' open available reports and external datasets, and several impact models. The primary purpose of this database is to support disaster management researchers working to develop and test network models that focus on road repair and restoration, and on the multi-modal distribution of relief supplies to victims. In addition, the data can be used to test heuristic and metaheuristic approaches applied to network models in the context of natural disasters.

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 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 score0.552
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.217
Teacher spread0.207 · 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.

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

Citations3
Published2022
Admission routes3
Has abstractyes

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