Flood vulnerability and risk assessment of historic urban areas: Vulnerability evaluation, derivation of depth‐damage curves and cost–benefit analysis of flood adaptation measures applied to the historic city centre of Tomar, Portugal
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
Abstract Around 45% of natural hazards reported worldwide are related to floods, and current indications show that exposure to floods and inherent losses will keep escalating. Historic centres are particularly vulnerable in this context due to the structural and material characteristics of the buildings and because they embrace social and cultural values that must be safeguarded. This article aims to contribute to this research area by presenting and discussing the application of an index‐based methodology specifically tailored to assess flood risk in historic urban centres. The historic city centre of Tomar, Portugal, an area that encompasses over 500 buildings and has a rich history of floods, is used here as a case study. Vulnerability data resulting from the application of the vulnerability assessment approach are then combined with flood hazard—that is, water velocity and depth obtained from flood peaks estimated for 20‐ and 100‐year periods of return—and used to identify the buildings at risk. Finally, a set of depth‐damage curves is derived and used here to carry out a cost–benefit analysis for different flood adaptation measures.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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