Using Vapor Generation Equipment to Create Artificial Rain: The Design and Function of a New System
Bibliographic record
Abstract
The incidence of water shortage events – including drought, forest fire, and desertification – is rapidly increasing due to global warming. This paper shows the principles and the practical application of a new artificial rain system that would help prevent these types of harmful water shortage events. The proposed artificial rain system is composed of solar-powered vapor generation equipment that floats on a large body of water. From this water, vapor is generated by means of solar energy. This vapor is transformed into clouds. These clouds are transported to an area experiencing water shortage, and these clouds provide rain to the target area. The proposed artificial rain system can be designed to provide a specific amount of rain, to be applied at a pre-determined time, to a specified area. This equipment is operated by solar power, so does not produce any CO2emissions. The detailed design example shown in this paper demonstrates that a vapor generation equipment group 1,080km square in area can make 1,200 kg of vapor per square meter per one year, and provide precipitation for an agricultural area 9,720 km square. The advantages and disadvantages of this system are considered. The estimated cost to produce one kilogramme of precipitation water by the proposed artificial rain system is about 0.002USD.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".