Ligurian Downslope Winds Assessed Using LiDAR Scanner for the Safety Management of Port Areas
Bibliographic record
Abstract
Abstract Severe weather conditions may affect the structural integrity of buildings and infrastructures in port areas. Recommendations provided in international codes are currently based on the quasi-steady atmospheric boundary layer (ABL) winds which are not entirely representative of all the winds that may occur in the lower atmosphere. For example, ABL winds are often characterized by a lower mean wind velocity near the ground compared to other types of winds such as downbursts, tornadoes, and downslope winds (DWs). DWs develop when cold air flows over a mountain ridge with a steep lee slope, where the orographic wave breaking commonly occurs, and the cold air further slides down the mountain. DWs are well-known to occur all over the world. Thus, investigating the nature of DWs is mandatory to improve the current standards and safely management procedures of port infrastructure and operations. In this study, a measurement campaign was carried out using the LiDAR installed in the Port of Genoa to analyze DWs descending from the Apennines to the Ligurian coastline through the Turchino Valley. LiDAR scans were conducted from December 2024 to early March 2025, when DWs occur more frequently. RHI and PPI scans were carried out during six strong DW events using azimuthal angles from 295° to 330°, scanning elevations from 0° to 15°, range gates from 0.3 to 14.3 km, and 100 m resolution. The representative results of the studied DW events are presented here.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".