Urban water resilience in Hindu Kush Himalaya: issues, challenges and way forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The urban population is expected to rise up to 68% by 2050, adding 2.5 billion people to the urban areas of the world. The majority of the rise is expected to be in the low-income countries of Asia and Africa. Several cities/towns in the Hindu Kush Himalaya (HKH) region are expanding at a rapid pace, putting additional pressure on water services and basic amenities for urban dwellers. Selected case studies undertaken by the authors suggest that the demand for water far exceeds municipal supply. Water governance in the HKH region remains a blind spot and challenges pertaining to urban water resilience are poorly understood. The paper is divided into three parts: the first outlines the development of towns and their water infrastructure through selected cases in the HKH, followed by key issues and challenges faced by urban systems and suggested measures to build urban resilience in order to deal with the projected rise in population, governance issues and anticipated changes in climate.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it