The December 2020 magnitude (Mw) 6.4 Petrinja earthquake, Croatia: seismological aspects, emergency response and impacts
Bibliographic record
Abstract
Abstract On December 29, 2020, nine months after the March Mw 5.4 Zagreb earthquake and amidst the COVID-19 lockdown, a devastating Mw 6.4 earthquake struck near the town of Petrinja, about 50 km SE from the country’s capital Zagreb. It was preceded by the Mw 4.9 foreshock from the day before. The main shock claimed 7 fatalities and caused widespread damage. Historical centers of nearby cities with invaluable heritage buildings were significantly affected as were the many residential buildings, built mainly of unreinforced masonry. Damage was observed as far as 60 km from the epicenter. This paper summarizes the seismological aspects of the Mw 6.4 Petrinja earthquake, the emergency response and the main impacts to people and buildings. The description and findings are based on the field observations and a series of post-earthquake activities led by the team of the Faculty of Civil Engineering, University of Zagreb. Typical damage to buildings and usability data are presented with examples based on 50,000 inspection results. By far the most affected were the unreinforced masonry buildings, followed by confined masonry, whereas reinforced concrete buildings were the least affected. The total direct and indirect losses are estimated to 4.8 billion EUR. The provided information represents a useful basis and impetus for improving emergency action and long-term disaster reduction plans in other regions with similar building exposure and seismotectonic settings.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".