Residential building stock in Serbia: classification and vulnerability for seismic risk studies
Bibliographic record
Abstract
Abstract Regional seismic risk assessment is necessary for designing effective seismic risk mitigation measures. In general, such risk assessment studies consist of three components: hazard, vulnerability, and exposure modelling. This paper lays the foundations for regional seismic risk assessment of the residential building stock in Serbia and addresses each of the three seismic risk assessment components, either by reviewing the existing or proposing novel models. First, a review of seismic hazard models and seismic design codes used in Serbia in the past 70 years was presented. Next, an overview of Serbia’s population metrics and historical development of Serbian’s residential building stock was presented to provide the context for the exposure model. Furthermore, the paper proposed a novel building classification for Serbia's residential building stock, which is based on the existing building taxonomies, but it has been adapted to account for the local building characteristics. Building damage patterns reported in past earthquakes in Serbia and neighbouring countries were reviewed as a basis for damage classification pertaining to building typologies included in the proposed classification. Finally, the results of a preliminary vulnerability model were presented in the form of expert-based fragility functions derived for buildings typical of Serbia's residential building stock.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".