Investigation of the Weather Conditions During the Collapse of the Morandi Bridge in Genoa on 14 August 2018 Using Field Observations and WRF Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
On 14 August 2018, Morandi Bridge in Genoa, Italy, collapsed to the ground that was 40 m below. This tragedy killed 43 people. Preliminary investigations indicated poor design, questionable building practices, and insufficient maintenance—or a combination of these factors—as a possible cause of the collapse. However, around the collapse time, a thunderstorm associated with strong winds, lightning, and rain also developed over the city. While it is unclear if this thunderstorm played a role in the collapse, the present study examines the weather conditions before and during the bridge collapse. The study particularly focuses on the analysis of a downburst that was observed around the collapse time and a few kilometers away from the bridge. Direct and remote sensing measurements are used to describe the evolution of the thunderstorm during its approached from the sea to the city. The Doppler lidar measurements allowed the reconstruction of the gust front shape and the evaluation of its displacement velocity of 6.6 m s−1 towards the lidar. The Weather Research and Forecasting simulations highlighted that it is still challenging to forecast localized thunderstorms with operational setups. The study has shown that assimilation of radar reflectivity improves the timing and reconstruction of the gust front observed by local measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it