READY: a web-based geographical information system for enhanced flood resilience through raising awareness in citizens
Bibliographic record
Abstract
Abstract. As evidenced by the EU Floods Directive (2007/60/EC), flood management strategies in Europe have undergone a shift in focus in recent years. The goal of flood prevention using structural measures has been replaced by an emphasis on the management of flood risks using non-structural measures. One implication of this is that public authorities alone not only take responsibility for flood management. A broader range of stakeholders, who may personally experience the negative effects of flooding, also take on responsibility for protecting themselves. Therefore, it is vital that information concerning flood risks is conveyed to those who may be affected in order to facilitate the self-protection of citizens. Experience shows that problems persist even where efforts have been made to communicate flood risks. There is a need for the development of new tools that are able to rapidly disseminate flood-risk information to the general public. To be useful these tools must be able to present information relevant to the location of the user. Moreover, the content and design of the tool need to be adjusted to laypeople's needs. Dissemination and communication influence both people's access to and understanding of natural risk information. Such a tool could be a useful aid to effective management of flood risks. To address this gap, a web-based geographical information system (WebGIS) has been developed through the collaborative efforts of a group of scientists, hazard and risk analysts and managers, GIS analysts, system developers and communication designers. This tool, called "READY: Risk, Extreme Events, Adaptation, Defend Yourself", aims to enhance the general public knowledge of flood risk, making citizens more capable of responding appropriately during a flood event. The READY WebGIS has allowed for the visualization and easy querying of a complex hazard and risk database thanks to a high degree of interactivity and easily read maps. In this way, READY has enabled fast exploration of alternative flood scenarios or past calamitous events. Combined also with a system of graphic symbols designed ad hoc for communication of self-protection behaviours, it is believed READY could lead to an increase in citizen participation, informed discussion and consensus building. The platform has been developed for a site-specific application: the Basilicata region, Italy, has been selected as pilot application area. The goal of the prototype is to raise citizen awareness of flood risks and to build social capacity and enhanced resilience to flood events.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".