A case study of boundary‐layer development downstream of a small, remote island
Bibliographic record
Abstract
Abstract Marine fog remains a challenging process for environmental prediction; in coastal areas, these challenges are complicated by the heterogeneity of the land–sea boundary. Here, the findings from an investigation into the boundary‐layer development downstream of a remote, low‐relief island will be presented. This work arose from an intense observing period (IOP) during the Fog And Turbulence Interactions in the Marine Atmosphere (FATIMA) 2022 field campaign near Sable Island (SI) in the North Atlantic. The premise was to examine a unique fog dissipation mechanism known as a fog shadow , hypothesized to be caused by fog‐laden boundary interaction with the island's surface. Using coordinated measurements from the island observing station and a heavily instrumented research vessel, we have conducted an extensive analysis into the spatio‐temporal evolution of the atmospheric boundary‐layer state downstream of SI. A low‐level jet during the IOP prevented the development of an extensive fog layer in the region, and hence localized dissipation in the lee of the island. In terms of boundary‐layer development, the measurements captured the rough–smooth internal boundary‐layer formation, with some secondary impacts of the temperature adjustment from land to water surfaces. Analysis of the fetch‐limited wave growth followed the expected self‐similarity, but at a much higher intensity than predicted for the stability regime; this was linked to spatial inhomogeneity in wind acceleration and stress downstream, which may have increased the lateral extent of island impacts on the boundary layer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".