Bibliographic record
Abstract
Fine-dispersed sprinkling, ultra-light water fog, and artificial snow are currently widely used in various sectors of the national economy. With a view to the possibility of a wider use of fine sprinkling and artificial snow in the agro-industrial sector and a practical proposal for their use, the authors analyzed the designs of technical devices capable of finely dispersing liquids and forming artificial snow for agricultural needs. The study determined the main technological parameters of the optimal technical solution. Studies have shown that artificial snow generators can be used not only for the production of artificial snow, but also for the implementation of fine sprinkling or the creation of ultra-light water mist, used as a covering material, maintaining the microclimate in agricultural buildings, treating agricultural plants with nutrients or protective solutions. The formation of artificial snow or fine sprinkling is carried out by stationary or mobile installations that provide water spray in the form of tiny droplets ranging in size from 50 to 600 microns, which tend to freeze later on. It has been determined that the most effective devices for spraying liquid in snow generators are sprayers based on the Laval nozzle, which provide high-quality fine spraying of the liquid with the ability to control the droplet size and ensure stable operation under various temperature conditions. The theoretical productivity of the plant during the treatment of crops and plantings with artificial snow can range between 0.05 and 0.34 ha/h. The conclusion is made about the need for further studies of the dynamic impact of the sprayed liquid jet on plants to determine the optimal operating modes and design parameters of the device, and the optimal location of the spraying devices relative to the treated objects.
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.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".