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Record W2895163584 · doi:10.1016/j.dib.2018.09.082

Measurement of wind field data in Southeast China

2018· article· en· W2895163584 on OpenAlexaff
Lin Li, Kai Chen, Dandan Xia, Huaifeng Wang, Haitao Hu, Fuqiang He

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsUniversity of Regina
FundersNatural Science Foundation of Fujian ProvinceChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsChinaField (mathematics)Research articleData scienceGeographyRemote sensingMeteorologyComputer scienceLibrary scienceArchaeologyMathematics

Abstract

fetched live from OpenAlex

The data presented in this article are the wind measurements acquired from a tower in Southeast China during typhoon Nesat (1709#) and typhoon Haitang (1710#). Three 3D ultrasonic anemometers Wind Master Pro were utilized to obtain 3D wind data. The anemometer works well with wind speed range of 0-65 m/s and wind angle range of 0-360°. Three direction wind speeds and wind angles were recorded per every 0.1 s. The present research analyzed wind characteristics based on recorded data. In this article, the detailed test set-up and data pre-processing methodology for the wind characteristics analysis are provided.

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)0.001
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 score0.072
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.049
GPT teacher head0.262
Teacher spread0.213 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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