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Record W2120654387 · doi:10.1002/joc.3769

Future convective environments using <scp>NARCCAP</scp>

2013· article· en· W2120654387 on OpenAlexaboutno aff
Vittorio A. Gensini, Craig A. Ramseyer, Thomas L. Mote

Bibliographic record

VenueInternational Journal of Climatology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorOffice of Research and DevelopmentNational Center for Atmospheric ResearchU.S. Department of EnergyU.S. Environmental Protection AgencyNational Aeronautics and Space Administration
KeywordsConvective available potential energyClimatologyConvectionConvective storm detectionEnvironmental scienceStormWind shearSevere weatherPopulationMeteorologyConvective inhibitionAtmospheric sciencesGeographyWind speedGeologyDemography

Abstract

fetched live from OpenAlex

ABSTRACT This study examines trends in atmospheric environments conducive to the development of severe convection in the United States, as simulated by a regional model forced with output from a global climate model. Meteorological variables necessary for severe convection from current (1981–1995) and future (2041–2065) epochs were compared. Results indicate a statistically significant increase in the number of significant severe weather environments in the Northeast United States, Great Lakes, and Southeast Canada regions. Regional severe weather environment increases can be attributed to both an increase in convective available potential energy (CAPE) and the number of times deep‐layer wind shear and CAPE juxtaposition. Given the current distribution of severe convective weather, these changes would alter the current physical risk of severe convective storms across a large population.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.266
Teacher spread0.251 · 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 designSimulation or modeling
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

Citations66
Published2013
Admission routes1
Has abstractyes

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