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Record W2060969674 · doi:10.1080/10807031003788634

Earthquake Insurance and Earthquake Risk Management

2010· article· en· W2060969674 on OpenAlexaff
Desheng Wu, Zhenlong Zheng, Xiaxin Tao

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUrban seismic riskEarthquake scenarioSeismic hazardActuarial scienceRisk managementPaymentProperty insuranceEarthquake casualty estimationInsurance policySeismologyBusinessCasualty insuranceGeologyFinance

Abstract

fetched live from OpenAlex

ABSTRACT The Wenchuan earthquake is the largest devastating earthquake striking China since the 1976 Tangshan earthquake. In this catastrophe, loss payments were mainly from the government and public endowment. The insurance industry is expected to take more responsibility in the future, since earthquake insurance is one of the most effective and equitable instruments to disperse earthquake losses. In this article, earthquake risk management and the development of earthquake insurance in China are reviewed. Earthquake insurance is suggested as an instrument in earthquake risk management, where the premium rate of earthquake insurance is a key factor that needs to be determined reasonably. Seismic hazard is analyzed for the Wenchuan earthquake-stricken area, and is combined with primary loss estimation to construct the exceeding probability curve. Earthquake insurance premium rates are calculated for buildings in the area, including RC (Reinforced Concrete), frame, and brick, corresponding to two kinds of insurance deductible.

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)0.001
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 score0.168
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.275
Teacher spread0.254 · 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 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

Citations9
Published2010
Admission routes1
Has abstractyes

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