MétaCan
Menu
Back to cohort
Record W2169422981 · doi:10.1193/1.1809130

A Challenge to Earthquake Engineering Professionals

2004· article· en· W2169422981 on OpenAlexaff
Craig D. Comartin, Svetlana Brzev, Farzad Naeim, Marjorie Greene, Marcial Blondet, Sheldon Cherry, Dina D’Ayala, Mohammed N. Farsi, Sudhir K. Jain, Jelena Pantelic, Laura Samant, Mauro Sassu

Bibliographic record

VenueEarthquake Spectra · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, financial, and policy analysis
Canadian institutionsUniversity of British ColumbiaBritish Columbia Institute of Technology
Fundersnot available
KeywordsSAFERVulnerability (computing)EngineeringForensic engineeringEnvironmental planningPolitical scienceGeographyComputer scienceComputer security

Abstract

fetched live from OpenAlex

Recent earthquakes have caused unacceptably high death tolls. We, the editors of the World Housing Encyclopedia, believe that reducing such an unacceptably high loss of life from earthquakes is the most important challenge facing the global earthquake engineering community. This paper acknowledges the continuing disparity between life loss from earthquakes in developing and developed countries, and the increasing vulnerability in developing countries. A sampling of current efforts to improve construction practices includes the publication of earthquake tips in India, construction manuals in Colombia, and the formation of various international networks to promote collaboration and information sharing. Future possibilities include more rewards for research into inadequately engineered construction, greater emphasis on small‐scale, local efforts, and a stronger emphasis on advocacy. We believe that all of us, as earthquake professionals, have a responsibility to make the built environment safer worldwide.

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)0.013
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.015
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.007
Scholarly communication0.0080.008
Open science0.0010.004
Research integrity0.0110.009
Insufficient payload (model declined to judge)0.0150.004

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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations24
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueEarthquake SpectraSame topicEconomic, financial, and policy analysisFrench-language works237,207