Effect of temperature on chlorine treatment for elimination of freshwater phytoplankton in ballast water: bench-scale test
Bibliographic record
Abstract
Chlorine efficacy as a biocide for ballast water treatment was investigated under cold- and warm-water temperatures across winter and summer seasons. Freshwater phytoplankton samples were collected and acclimated under in situ environmental conditions ranging from 2 to 22 °C. Samples were exposed to seven chlorine treatments (from 0.02 to 5.0 ppm), in addition to a control (0.0 ppm). Free-chlorine concentrations, phytoplankton abundance, and photosynthetic efficiency were measured up to 48 h following treatment. After 4 h of treatment at concentrations less than 0.22 ppm, phytoplankton densities were reduced by more than 50%, without cell resurgence. Similar reduction was recorded immediately after exposure when chlorine concentrations were higher than 3.0 ppm. After 8 h, free chlorine neared 0.0 ppm for initial chlorine concentrations below 1.2 ppm, irrespective of temperature regime. Winter phytoplankton exhibited slightly lower mortality to chlorine exposure regardless of the temperature, although they also exhibited lower photosynthetic efficiency. Despite a general absence of significant effect of temperature on the chlorine decay, our results suggest that higher doses of chlorine or longer exposure times may be required during winter to achieve full treatment effect. Tests at large scales are needed to further confirm these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".