DOE-ANOVA Analysis to Estimate the Effect of Ambient Temperature, Pressure and Humidity on Surface Wind Speed
Bibliographic record
Abstract
The traditional assessment of wind resource considers the ambient factors like temperature, pressure and humidity as a secondary parameter, and focus on the statistically evaluation of wind magnitude and direction.Nevertheless, the evaluation requires a deeper analysis of the above ambient factors, because they are thermodynamically linked, and the energy flux among them will affect the wind dynamics.This research aims to analyse the interactions of temperature, pressure, and humidity variables with the wind speed with an inverse approach, it means that wind will be controlled by the ambient variables.We conducted a statistical analysis using data from a meteorological station located in the University of the Coast in Barranquilla, Colombia.The methodology is based on a Design of Experiments-Analysis of Variance (DOE-ANOVA) with a 3 2 factorial design.The estimated effects from the DOE-ANOVA results were utilized to generate standardized effect equations, for evaluating the response of wind speed during changes of the studied ambient variables (factors).The results evidenced that the applied methodology provided information about the non-linear interactions of the analysed variables, and the standardized effect equations were tested against a liner regression model with satisfactory results.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".