Electrostatic Particle Ionization for Reduction in Livestock and Potash Dust
Bibliographic record
Abstract
Airborne dust is an important contaminant affecting the health and the environment, and a crucial concern in many workplaces such as animal facilities and potash mines. One of the techniques used for dust control is electrostatic particle ionization (EPI). This technology has been proven effective in reducing airborne dust; however, it has downsides, such as the generation of ozone and corrosion of electrodes. Thus, this study tested a corrosion-resistant carbon-fiber discharge electrode and compared it with electrodes commonly used in EPI systems, that is, stainless-steel and tungsten electrodes, in terms of collection efficiency for potash dust and wheat flour (representative of livestock dust), ozone production, and power consumption. The carbon-fiber electrode performed comparably to stainless-steel electrodes, particularly for potash dust, and performed better than the tungsten electrode in terms of dust collection efficiency. Moreover, it had the lowest energy consumption and generated the least amount of ozone. However, because of the limitations of this study (e.g., fewer samples, low air velocity, controlled conditions, and the use of wheat flour instead of livestock dust), tests under real barn or mining conditions are necessary to confirm the results.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".