Research History and Functional Systems of Fog Water Harvesting
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
Water is among the top five global risks in terms of impacts translated through socio-economic and environmental challenges, influencing people's wellbeing. The situation is grim in water-scarce countries, which need to think and act beyond conventional water resources and tap unconventional water supplies to narrow the gap between water demand and supply. Among unconventional water resources, water embedded in fog is increasingly seen as a source of potable water in dry areas where fog is intense and prevalent. Although a low maintenance option and a green technology to supply freshwater, the potential to collect water from air through fog harvesting is by far under-explored. Based on the comprehensive analysis of fog water collection's research history since 1980, this study reveals that recent years have witnessed a sharp increase in research related to technological developments in fog collection systems. Also, there is an increased focus on associated policy and institutional aspects, economics, environmental dimensions, capacity building, community participation, and gender mainstreaming. In addition to research, fog water collection practice has also increased over time with emerging examples worldwide, notably from Canary Islands, Chile, Colombia, Eritrea, Ethiopia, Guatemala, Israel, Morocco, Namibia, Oman, Peru, and South Africa. The functional systems of fog water collection demonstrate community engagement, women empowerment, enhanced capacity and training, and active participation of local institutions as the key drivers for effective fog collection systems to provide a sustainable supply of freshwater to the associated communities.
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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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it