The UNESCO IHP FRIEND-Water programme: a global network for hydroclimatic change research and education 
Bibliographic record
Abstract
The Flow Regimes from International Experimental and Network Data (FRIEND-Water) is the oldest UNESCO Flagship Initiative within the Intergovernmental Hydrological Programme (IHP). Active since 1985, it seeks to facilitate, promote and foster collaborations across borders between scientists (hydrologists and related disciplines) to conduct studies on shared river basins. The programme has evolved over time to focus on four key themes relating to (i) data collection and sharing, (ii) the impacts of global change on hydrological regimes and extremes, (iii) water-society interactions and equitable water management and (iv) interdisciplinary educational resources and programmes. Involving researchers from over 150 countries, FRIEND-Water is currently structured into six regional groups around the world of which four focus on Europe-African collaboration: Europe, the Mediterranean, West and Central Africa, Southern and Eastern Africa. Collaborations include joint research activities, joint supervision of young researchers (PhD and postdoc), exchange visits and scientific events. In partnership with initiatives such as CEH Robin, WMO HydroSOS, IHP-WINS and GRDC, activities notably focus on increasing the collection and sharing of hydroclimatic data across FRIEND-Water regions. Hydrometry training, data rescue, and ongoing collection of hydrological data from ground observation networks are actively supported. Researchers explore large-scale climate and hydrological regime trends as well as the local impacts of future climate projections from CMIP5/CMIP6 models. Hydrological modelling helps forecast the amplitude and frequency of extreme events (floods, agricultural droughts and compound extremes) and support disaster risk reduction and early warning systems. Working on urban and rural areas, research also seeks to define adequate hydrological norms (accounting for climate non-stationarity) and guide the design of water infrastructure, as well as water management and allocation policies. Activities over the past decade have notably led to the joint EU-African organization of over 30 workshops and trainings on topics including early warning systems, hydrological modelling, hydrometry, as well as four conferences on the Hydrology of African Large River basins. Going into UNESCO IHP-IX, the FRIEND-Water programme has been restructured and is now actively supported by the UNESCO Category II Centre ICIREWARD in Montpellier, leading to increased academic collaboration and capacity building opportunities between Europe and Africa.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".