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Record W2088218032 · doi:10.1139/l09-154

Influence of spatial inhomogeneity of tornado occurrence on estimated tornado hazard

2010· article· en· W2088218032 on OpenAlexafffundvenueabout
L. Tan, Han Hong

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTornadoReturn periodEnvironmental scienceWind speedMeteorologyHazardSpatial distributionStatisticsGeographyMathematicsFlood myth

Abstract

fetched live from OpenAlex

Tornado hazard assessment is often based on the consideration that the spatial distribution of tornado occurrence is homogeneous in a region. Although this assumption simplifies the analysis, it could overestimate and underestimate tornado hazard for regions with lower and higher tornadic activity if an average rate of tornado occurrence is employed. The degree of overestimation and underestimation is unknown. This study is focused on the assessment of the impact of spatial inhomogeneity of tornado occurrence on the estimated tornado hazard and the development of tornado hazard maps for southern Ontario. The results indicate that at the factored design wind speed the exceedance probability for tornadic winds is much smaller than that for synoptic winds, even if the spatial inhomogeneity of tornado occurrence is considered. Furthermore, the results show that the spatial inhomogeneity of tornado occurrence has significant impact on the spatial tornado hazard level, that the return period values of tornado wind speed vary significantly over the considered region, and that the inhomogeneity must be considered in developing probabilistic quantitative tornado hazard maps.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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.

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

Citations8
Published2010
Admission routes4
Has abstractyes

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