Bibliographic record
Abstract
Abstract Observations and cloud-resolving simulations of elongated cloud plumes (or “cloud trails”) past the Lesser Antilles islands in the Caribbean Sea are presented. Analysis of one year of visible satellite images reveals that each island forms cloud trails on 30%–40% of days, typically in the afternoon in response to diurnal island heating. On around 10% of days the cloud bands are very well organized, with lengths of and durations of min. Radiosonde analysis suggests that the well-organized events are favored by moderate-to-strong trade winds (6–10 m s−1) and stronger trade inversions. The simulated cloud trails, which are consistent with observations in their morphology and diurnal cycle, are organized by quasi-linear bands of thermally forced convergence within the heated island wake. They are sensitive to overland surface fluxes, inversion strength and height, terrain height, and trade-wind speed. While surface fluxes control the strength of the wake thermal circulations, the inversion controls precipitation and the disruption of cloud trails by subcloud cold pools. The impacts of terrain height and wind speed are multifaceted, including control over (i) the mechanical flow regime, (ii) the intensity of wake turbulence, (iii) the cloud-trail length, (iv) the wake thermal anomaly, and (v) elevated-heating effects (which strengthen the thermal convergence). Dimensional analysis is used to develop empirical scalings for the wake thermal circulation, which describe the suite of numerical sensitivity tests reasonably well.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".