Bibliographic record
Abstract
Dr. András Szöllősi-Nagy is an internationally renowned professor, by qualification an engineer, hydrologist. Doctor of the Hungarian Academy of Sciences (management of hydrology and water management systems). He has been a visiting professor at several foreign universities (Sweden, Canada, Thailand, China). For twenty years he was Secretary of the UNESCO International Hydrology Programme in Paris, and he was Deputy Director- General of UNESCO’s Natural Sciences Sector for ten years. For the past five years, he has been rector of the UNESCO-IHE Institute of Water Science in Delft, the Netherlands. He is professor of Stochastic Hydrology at Delft University of Technology and at the UNESCO-IHE. His main research interests are stochastic hydrological systems and recursive forecasting algorithms, as well as the hydrological effects of sustainable development and climate change. Lecturer at the Faculty of Water Sciences of the University of Public Service, Honorary Doctor of the University of Debrecen. His professional opinion and resolutions – given his professional career so far – serve as a credible and effective guide for responsible water professionals. We asked the professor about how science serves national water management, what has happened so far and what we still need to do to conserve available water resources, and how extreme situations and epidemics affect these activities.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.119 | 0.039 |
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".