Flood risk assessment and prioritisation of measures: two key tools in the development of a national programme of flood risk management measures in Moldova
Bibliographic record
Abstract
Following severe floods in 2008 and 2010, the Government of Moldova requested assistance to improve flood protection throughout the country.The European Investment Bank has funded a Technical Assistance project to develop a programme of flood risk management measures.The project included the detailed 2D hydraulic modelling of the high-risk rivers (about 3400 km) to produce flood hazard and flood risk maps, the identification of measures to reduce the flood risk, the prioritisation of measures and the development of a phased investment programme and a Short-Term Investment Plan.Flood risk was assessed using 12 flood risk indicators representing social, economic and environmental impacts of flooding.Prioritisation of measures took account of: (i) the urgency of the measure; (ii) the magnitude of the risk that can be reduced with the measure; (iii) the benefit-cost ratio of the measure.The approaches used and in particular the methodologies implemented and the results obtained for flood risk assessment and for prioritisation of measures proved to be valuable tools to reach the objective of the study and, in particular, to facilitate the discussion with the stakeholders and the decision-making process.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".