Formulation of an Index for Sea Breezes in Opposing Winds
Bibliographic record
Abstract
Abstract A scaling analysis is conducted to explore the dependence of sea-breeze speed and inland occurrence in the presence of opposing winds on a set of dynamical parameters. The overall aim of the analysis is to develop an index for sea-breeze occurrence in the face of opposing winds, similar to the Biggs and Graves lake-breeze index. Most studies separate sea-breeze speed and sea-breeze inland occurrence or, at best, link the two in linear analyses. This work analyzes the output of a nonlinear numerical mesoscale model (in idealized simulations) using scaling methods commonly applied in observational studies. It is found that the scaled sea-breeze speed, in response to increasing magnitude of opposing wind, shows two distinct phases: a phase of increasing speed while the sea breeze progresses inland and a phase of sharply decreasing speed when the sea breeze is no longer detected inland. The analysis also allows the development of an index for sea-breeze inland occurrence. This index is an improvement over existing analyses through the use of nonlinear scaling and the use of surface heat flux as opposed to simpler land–sea temperature contrasts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".