The role of Dutch expertise in Romanian water projects. Case study "Integrated water management for the Tecucel River Basin"
Bibliographic record
Abstract
Floods are the most important natural risk in Romania. They occur almost on a yearly basis and cause major economic damage and casualties. The project ‘Integrated Water Management for the Tecucel River Basin’ was formulated in response to a flood in the city of Tecuci and its surroundings in 2007. Due to heavy rainfall, water levels on the small Tecucel River increased within a few hours. This caused a major flash flood that affected nearly 60% of Tecuci. At that time, a Romanian student did an internship at water board Hunze and Aa’s (WB H&A) in the North of the Netherlands. She informed employees and the management board of WB H&A about the flood. The water board decided to look for possibilities to do something to prevent the occurrence of similar floods in the future. As the floods also affected the delivery of drinking water and the treatment of wastewater, it decided to adopt an integrated approach. Together with five other organizations, it formulated a project that would improve the water system and living conditions in the Tecucel River Basin and enhance bilateral collaboration and knowledge transfer. Following an exploratory visit and a preparatory mission by Dutch experts (2007 and 2008), the Dutch team submitted a project proposal to the Dutch funding agency Partners for Water. At that time, the agency was not able to fund projects. Hence, the same proposal was submitted to the Netherlands Water Board Bank. This bank could cover up to 50% of the project costs. The remaining costs were covered by the Dutch organizations involved. This report presents the above-mentioned project as a case study within the context of a PhD research on the application of Dutch knowledge in Dutch-funded flood risk projects in Romania.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".