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Record W4288766319 · doi:10.26443/msurj.v17i1.171

Overblown? Analyzing Wind Speed in the Hurricane Warning Response System

2022· article· en· W4288766319 on OpenAlexaff
Killian Abellon, Amelia Murphy, Anika Anderson

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsMcGill University
FundersFederal Emergency Management Agency
KeywordsStorm surgeDamagesWind speedHazardStormLandfallEnvironmental scienceWarning systemClimatologyMeteorologyScale (ratio)Atlantic hurricaneResilience (materials science)GeographyEnvironmental resource managementCartographyComputer scienceGeologyPolitical science

Abstract

fetched live from OpenAlex

The role of wind speed in determining the impacts of hurricanes is examined via statistical analysis of Cate- gory 2-5 hurricanes that made landfall in the U.S. Atlantic basin coastline, including Puerto Rico’s coast, from 1970-2020. The results indicate a positive yet statistically insignificant correlation between wind speed and hurricane deaths, cost of damages and federally obligated recovery aid. Other factors, such as storm surge, rainfall, and inland inundation, may be more strongly correlated with these impacts. The results are contextualized by a wealth of literature pointing to the role of social, political, and economic factors in determining the destructiveness of hurricanes. Finally, alternative indices to the popular Saffir-Simpson hurricane hazard scale – which relies on wind speed – are examined. As climate change advances and hurricanes become in- creasingly frequent and severe, more comprehensive hazard-rating scales may provide the basis for a more effective warning-response system, ultimately bolstering the resilience of coastal areas.

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)0.024
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.007
Science and technology studies0.0090.001
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.003
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 head0.056
GPT teacher head0.328
Teacher spread0.271 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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