The Mw5.4 Zagreb (Croatia) earthquake of March 22, 2020: impacts and response
Bibliographic record
Abstract
5.4 Zagreb earthquake. Located within the city limits at a depth of 10 km, the earthquake generated a peak ground acceleration of more than 0.2 g and a maximum spectral acceleration of about 0.6 g at 0.1 s in the historic downtown area. The situation was particularly challenging since the event occurred amid a partial Covid-19 lockdown at temperatures close to 0 °C, emphasizing the extensive and complex vulnerability of the local communities and individuals. 27 people were reported severely injured, one of which later died. The surprisingly high economic costs, needed to achieve a full reconstruction of damaged buildings and infrastructure in the affected area, are currently evaluated at more than 10B euros. Description of the organization of the emergency response in the first days and the observed damage to buildings is given with typical examples. The focus is on the performance of older masonry residential and cultural heritage buildings in the historic downtown, their inspection and evaluation of damage to structural and non-structural components. This information provides the basis for understanding of the negative impacts and clarifies the overall context identifying the enablers and barriers to the still ongoing recovery process. It also helps to increase the awareness of the seismic vulnerability of European cities with similar construction practices.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".