Inter-Basin Water Transfer: Case Studies from Australia, United States, Canada, China and India
Bibliographic record
Abstract
Foreword Overview and scope Acknowledgements List of abbreviations Part I. The Challenges: 1. World population and pressures on land, water and food resources 2. Issues in inter-basin water transfer Part II. Inter-basin Water Transfer in Australia: 3. Land and water resources of Australia 4. The Snowy Mountains hydro-electric scheme 5. Inter-basin water transfer from coastal basins of New South Wales 6. The Bradfield and Reid Schemes in Queensland 7. Three schemes for flooding Lake Eyre 8. The Goldfields pipeline scheme of Western Australia 9. Supplying Perth, Western Australia with water: the Kimberley pipeline scheme 10. Other schemes in Australia Part III. Inter-basin Water Transfer in Other Selected Countries: 11. Inter-basin water transfer in the United States of America 12. Inter-basin water transfer in Canada 13. Inter-basin water transfer in China 14. India: the National River-Linking Project 15. Inter-basin water transfer, successes, failures and the future Part IV. Appendices: A. Some of the Australian pioneers of inter-basin water transfer B. Construction timetable of the Snowy Mountains Hydro-electric Scheme C. Details of diversion schemes from the Clarence River Basin D. Chronological table of the most important events in the Goldfields Pipeline Scheme, Western Australia E. Flooding of the Sahara depressions F. The Ord River Irrigation Scheme G. The West Kimberley Irrigation Scheme H. Some other water transfer schemes in Australia I. Selected technical features of the Central Valley Project in California J. Selected technical features of the State Water Project in California K. Selected characteristics of some of the completed or proposed inter-basin water transfer projects in Australia, United States, Canada, China and India, in chronological order Glossary Index.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".