Three-Dimensional Wind Correlation: Estimations from In Situ Measurements
Bibliographic record
Abstract
The correlation properties of the wind (expressed as wind coherence in frequency domain representation) are important for precise prediction of response to wind for flexible, line-like structures such as long-span bridges, telecommunication towers, wind turbine towers and transmission lines. Although extensive research including field measurements has been carried out in the past, there is still a lack of data that defines complete correlation between the components of wind turbulence. Whereas correlations in the along-wind mean direction are more or less clearly understood and well defined, there is a certain shortage of full scale data in the cross-properties among along-, vertical- and across-wind directions. It is therefore desirable to obtain more information related to these important wind parameters based on field measurements. The objective of this paper is to provide more information on this subject based on two in-situ wind measurements. One set of data was obtained during the measurements undertaken for the New Cooper River Bridge, Charleston, South Carolina. The second set is the wind measured on the experimental line of Hydro-Québec Research Institute (IREQ). The first site is characterized by open water for wind directions normal to the bridge crossing, i.e., close to south and north winds, whereas the second site was an open terrain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".