Fog Water Collection: Challenges beyond Technology
Bibliographic record
Abstract
The Sustainable Development Goal (SDG) 6, calling for access to safe water and sanitation for all by the year 2030 supports the efforts in water-scarce countries and regions to go beyond conventional resources and tap unconventional water supplies to narrow the water demand-supply gap. Among the unconventional water resources, the potential to collect water from the air, such as fog harvesting, is by far the most under-explored. Fog water collection is a passive, low maintenance, and sustainable option that can supply fresh drinking water to communities where fog events are common. Because of the relatively simple design of fog collection systems, their operation and maintenance are minimal and the associated cost likewise; although, in certain cases, some financially constrained communities would need initial subsidies. Despite technology development and demonstrated benefits, there are certain challenges to fog harvesting, including lack of supportive policies, limited functional local institutions, inexpert communities, gender inequality, and perceived high costs without undertaking comprehensive economic analyses. By addressing such challenges, there is an opportunity to provide potable water in areas where fog intensity and duration are sufficient, and where the competition for clean water is intensifying because water resources are at a far distance or provided by expensive sources.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".