Assessment of Ozone Nanobubble Technology to Reduce Freshwater Algae
Bibliographic record
Abstract
Algal blooms can lead to low dissolved oxygen and fluctuating pH, and the toxins produced by some species can be toxic to aquatic animals. In this study, we assessed the potential of ozone nanobubble technology for reducing a diatom species, Nitzschia sp., commonly found in freshwater ponds in Hong Kong. This study suggests that ozone at a low dose of 0.025 ± 0.003 ppm was sufficient to reduce algae by 66.4% within 5 minutes of treatment. An even higher killing effect (68.2%) was observed when ozone was delivered using nanobubbles for 9 minutes. A 24-hour delayed effect was also detected, with a further reduction of approximately 10% of the algae for both ozone treatments (macro and nanobubble delivery methods). In addition to controlling algae, applying ozone at a level that is not detrimental to fish may also benefit the dissolved oxygen levels in pond systems.
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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) | 0.000 | 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 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".