Looking Ahead to the 5 <sup>th</sup> World Water Forum — Istanbul 2009
Bibliographic record
Abstract
This paper will discuss preparations by EWRI and IWR for the 5th World Water Forum. Several potential WWF5 themes are being considered. Currently, the WWF5 sessions are divided into several broad thematic areas, including: 1. Water Security Adapting to Climate Change and Risk Management Protecting Resources, Livelihoods and Biodiversity Financing Water Infrastructure and Services; 2. Water Management and Governance Improving Management of Water Services and Resources Improving Water Governance Innovation and Water Technologies Equity, Education and Ethics; 3. Water Use and Impacts Health, Sanitation and Water Supply Food Security, Water for Food, and Ecosystems Water for Energy — Energy for Water; 4. Basin Management and Development; 5. Wild Card Themes. Although planning for US particpation is just beginning, EWRI has engaged both the AWRA and Corps of Engineers in discussions about our possible approach. One rich source of material may be the AWRA sponsored US Water Policy Dialogue in possible combination with Canadian and Mexican policy views. ASCE has taken positions in several areas of water policy as well. To prepare for our World Water Forum involvement, the Corps, AWRA and EWRI are planning a series of meeting to characterize key issues and flesh out important questions facing water managers and policy-makers in North America.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.090 | 0.022 |
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".