The Canadian Global Water Futures programme – creation, foundation, and operation
Bibliographic record
Abstract
The world is entering an era of immense water-related threats, from floods, droughts, and other extreme events to degradation of water quality and severe pressure on aquatic ecosystems, increasing demand for freshwater, and major changes in hydrological regime and water availability. There is a need for new science, new modelling tools and monitoring systems, and more effective mechanisms to translate new scientific knowledge into societal action. This represents a grand challenge for water science in Canada and globally: ‘How can we best prepare for and manage water futures in the face of dramatically increasing risks?’ The Global Water Futures (GWF) programme was conceived in 2015 to address this grand challenge; funded by the Canada First Research Excellence Fund and bringing together a transdisciplinary team of partners from 18 Canadian universities working with over 500 other partner institutions and organisations, GWF was the largest programme of its kind in Canada, and to our knowledge the largest in the world. As the programme ended in August 2025 this paper looks back at how GWF was conceived and developed, it’s management and operation, how it was structured to pursue an ambitious science agenda and how this was carried out, Indigenous engagement and knowledge exchange within GWF, changes in direction and approach as GWF evolved and circumstances arose, and lessons learned upon reflection. This was a vast effort by a large group of people and many partners and much of what was done was new and unique. By documenting our experience, this paper aims to serve as a useful guide for large science programmes.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".