Case study of foehn events over Alborz mountains in Iran
Bibliographic record
Abstract
<!--!introduction!--> In mountainous regions worldwide, warm and dry Foehn winds can have a significant impact on human life. The characteristics of foehn winds include a rising temperature, decreased relative humidity, and a persistent high wind direction of origin. Many parts of Iran, a country with mountainous terrain, are impacted by foehn winds. Iran's northern region is bounded by the Caspian Sea to the north and the southern Alborz Mountains. This study focuses on the southwestern part of the Caspian Sea coast. Significant mountain waves with large amplitudes have been observed, leading to severe forest fires in the Caspian region. The study shows that foehn events can arise as a result of high pressure in interior regions and a lee cyclone over the southern Caspian Sea, accompanied by a strong south-north pressure gradient across the Alborz Mountains. To demonstrate this foehn situation, four typical wind events from 2021 will be used as examples. The sample simulation results demonstrate the efficacy of WRF in forecasting changes in meteorological quantities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".