Evaluating technological resilience of small drinking water systems under the projected changes of climate
Bibliographic record
Abstract
With prevailing changes in climate and increasing population, small drinking water systems in the climate-vulnerable parts of the world have already exhibited and left traces of prominent unpredictability of water availability, in terms of both water quantity and water quality. Dimensions of climate change, such as large variability in precipitation pattern, rise in temperature and associated increase in evaporation rates, as well as their consequences, are surely going to affect the unsophisticated, small drinking water systems which are serving mass populations in South Asia and many other parts of the world. This research paper aims to analyze the possible extents of vulnerability of some selected small drinking water systems currently being operated in the coastal areas of Bangladesh as a result of the predicted changes in climatic parameters such as temperature, precipitation and evaporation along with sea level rise and extreme events such as cyclones. However, to examine possible future climate change scenarios, four Global Climate Models have been applied in developing projections of different climatic parameters for Bangladesh. Based on the projections of climate models and associated key vulnerabilities being assessed, this paper features the evaluation of potential technological resilience of specific small drinking water systems from the Bangladesh perspective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".