Effectiveness and potential environmental impact of a yeast-based deoxygenation process for treating ship ballast waters
Bibliographic record
Abstract
We assessed the effectiveness and potential environmental impact of a yeast-based deoxygenation process considered for treating ship ballast waters to reduce the risk of aquatic species introduction. Laboratory experiments were conducted to test three treatment concentrations (0.33%, 0.67% and 1.0% v v−1) at five temperatures (4, 10, 15, 20 and 25 °C) in both fresh- and saltwater, with and without mixing. Complete anoxia (<0.3 mg L−1) was achieved in all experiments, and there were no significant differences in effectiveness between fresh- and saltwater or between mixing levels. Time to hypoxia was inversely related to temperature, ranging from half a day at 25 °C to nearly 7 days at 4–5 °C. The process can quickly generate and maintain anoxic conditions over a long enough period of time to effectively eliminate a wide variety of aquatic organisms. Results of six bioassays indicated that treated waters were not toxic at the end of experiments and would not pose a toxic risk to natural receiving waters. Increased concentrations of ammonia, organic carbon and particulate matter resulting from yeast production in treated waters may cause some potential adverse environmental effects. The practicality of implementing this process for treating ballast water in ships is discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".