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Record W2789348375 · doi:10.9798/kosham.2018.18.2.505

Analysis for Vertical Wind Shear Change at Coastal Area according to the Sea Surface Temperature

2018· article· en· W2789348375 on OpenAlexaff
Geon Hwa Ryu, Joo Suk Ko, Meung Gi Baek, Jong Kyung Jang

Bibliographic record

VenueKorean Society of Hazard Mitigation · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFuture Earth
Fundersnot available
KeywordsWind shearWind speedSea surface temperatureEnvironmental scienceWind gradientLog wind profileWind directionPlanetary boundary layerGeologyClimatologyWind stressMeteorologyBuoyAtmospheric sciencesOceanographyGeography

Abstract

fetched live from OpenAlex

This study is to analyze the effect of the sea surface temperature (SST) on the distribution of vertical wind speed in the atmospheric boundary layer of coastal area. It is generally known that coastal areas are more susceptible to various weather factors due to interannual variation of sea surface temperature than inland areas. Therefore, the goal of this study is to analyze the relationship between sea surface temperature using the Era-interim reanalysis data and wind speed data based on the meteorological tower data of Hovsore, wind power test bed area in the Danish coastal area. Furthermore, the possibility of disaster caused by vertical wind shear due to sea surface temperature change is discussed. As a result of correlation analysis between the wind data of the meteorological tower and the sea surface temperature of the reanalysis data, the wind speed and the vertical wind shear from the sea are stronger than those from the inland and they are sensitive to the seasonal sea surface temperature changes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.352

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)0.0000.000

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 head0.021
GPT teacher head0.236
Teacher spread0.216 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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