Predictive Anisotropy of Surface Winds by Linear Statistical Prediction
Bibliographic record
Abstract
This study considers characteristics of the statistical predictability of surface wind vectors by linear regression using midtropospheric climate fields as predictors. Specifically, predictive anisotropy, which refers to unequal predictability of wind components projected onto different directions, is considered. The spatial distribution of predictability of surface wind components is determined at 2109 land surface meteorological stations across the globe. The results show that predictive anisotropy is a common feature that is spatially organized in terms of both magnitude and direction. The relationships between predictability and potential influential factors (topographic complexity, mean surface wind vectors, and standard deviation and kurtosis of wind components) are considered. It is found that poor predictability of wind components is generally associated with wind components characterized by relatively weak and non-Gaussian variability. While predictive anisotropy is often found in regions characterized by complex topography, marked predictive anisotropy also occurs away from evident surface heterogeneity. The relationships between predictability, variability, and shape of distribution of surface wind components are described using an idealized statistical model of large-scale and local influences on surface wind.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".