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Record W1766139619 · doi:10.1029/2010gl045052

Importance of location for describing typical and extreme wind speed behavior

2010· article· en· W1766139619 on OpenAlexaff
B. Griffin, Karen E. Kohfeld, Andrew B. Cooper, Gerhard Boenisch

Bibliographic record

VenueGeophysical Research Letters · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWind speedEnvironmental scienceMeteorologyWind powerClimate changeClimatologyGlobal wind patternsPercentileExtreme value theoryAtmospheric sciencesGeographyGeologyOceanographyStatisticsMathematicsEcology

Abstract

fetched live from OpenAlex

Several recent studies have considered the potential impact of climate change on regional wind intensity. However, previous wind speed studies in the Pacific Northwest (PNW) present conflicting results for wind speed trends in relation to climate drivers. This study analyzes the percentiles (50th, 75th, and 95th) of the strongly positively skewed distributions for PNW maximum daily wind speeds from 92 meteorological stations, and reveals different behaviors for average and extreme wind speeds. Considerably stronger winds are found at coastal locations compared with sites further inland. Extreme wind speeds at these coastal locations appear to follow an eight to nine‐year cyclic pattern, while mainland sites have a small, linear downward wind speed trend. This finding of a behavioral dependence on location helps reconcile previous, apparently contradictory results and has important global implications for wind research and infrastructure planning, such as wind energy feasibility studies and air quality management activities.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.115
GPT teacher head0.332
Teacher spread0.217 · 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

Citations20
Published2010
Admission routes1
Has abstractyes

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