Editorial: Resiliency of urban systems to water-related disasters
Bibliographic record
Abstract
As the world continues to urbanize at an unprecedented pace, cities face mounting challenges, one of which is the increasing frequency and intensity of water-related disasters (Benfield, 2016). The devastating impacts of floods, storm surges, and rising sea levels pose significant threats to urban systems and the wellbeing of their inhabitants. To safeguard the society, economy and environment against these risks, cities must prioritize the development and implementation of resilient strategies. In the context of urban systems, resiliency can be defined as how the urban system anticipates, absorbs, recovers, and adapts to vulnerabilities due to water-related disasters while maintaining essential functions. Resiliency is crucial for reducing vulnerability and minimizing the long-term impacts of water-related disasters (Hoekstra et al., 2018). It should be noted that there is not yet a unified definition of resilience in the literature, and the above-mentioned definition is an example among concepts and ideas available in the scientific literature related to resilience. Therefore, different research papers may use slightly different definitions.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".