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Record W2134224627 · doi:10.1139/l07-001

Tornado hazard assessment for southern Ontario

2007· article· en· W2134224627 on OpenAlexfundvenueaboutno aff
Sujoy Banik, Han Hong, Gregory A. Kopp

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsInstitute for Catastrophic Loss Reduction
KeywordsTornadoWind speedMeteorologyFujita scaleReturn periodEnvironmental scienceHazard analysisNatural hazardHazardEngineeringStatisticsGeographyMathematicsReliability engineering

Abstract

fetched live from OpenAlex

Southern Ontario experiences a number of natural hazards including tornado. However, quantitative tornado hazard assessment in terms of annual probability of exceeding a given wind speed has not been reported for this region. To carry out such a tornado hazard assessment, a statistical characterization of tornado parameters in southern Ontario has been developed using the tornado database of Ontario and that of the neighbouring regions in the United States. These parameters include the tornado occurrence rate, intensity, path length and width, and motion direction. Using the developed statistics and an existing wind field model, a probabilistic assessment of the tornado hazard in terms of wind speed is obtained for southern Ontario. The analysis suggests that for point-like structures such as residential structures, which are usually designed for a 30 year return period wind speed with a load factor of 1.5, the probability that a tornado wind speed exceeds the factored 30 year wind is very low. However, for line structures or elongated systems such as transmission lines, the tornado hazard becomes significant and increases with an increase in the length of the system. Key words: tornado hazard, wind speed, transmission line, probability, wind field, southern Ontario.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.194
Teacher spread0.187 · 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

Citations22
Published2007
Admission routes3
Has abstractyes

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