Addressing the unanswered questions in global water policy: a methodology framework
Bibliographic record
Abstract
Abstract Are the available water resources sufficient to produce food for the growing world population while at the same time meet increasing municipal, industrial and environmental requirements? Projections for the year 2025, presented by different research groups at the second World Water Forum in The Hague, show an increase in global agricultural water use ranging from 4 to 17%. Estimates for the growth of total withdrawals, including domestic and industrial sectors, vary from 22 to 32%. This range is the result of differences in model structure and assumptions. Although these analyses were instrumental in raising awareness concerning the extent of present and future water scarcity problems, they raise many questions, which remain largely unanswered. The questions relate to the impact of water‐ and food‐related policies on global and regional water scarcity, food production, environment and livelihoods through the year 2025. The International Food Policy Research Institute (IFPRI) and the International Water Management Institute (IWMI) embarked on a joint modeling exercise to address these questions. This paper lays out the issues and discusses the methodology. During the 18th ICID Congress in July 2002 at Montreal, preliminary results will be presented. Copyright © 2003 John Wiley & Sons, Ltd.
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 | 0.118 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".