Onboard ship evaluation of the effectiveness and the potential environmental effects of PERACLEAN® Ocean for ballast water treatment in very cold conditions
Bibliographic record
Abstract
This study verified the effectiveness and the potential toxic impact of PERACLEAN Ocean ballast water treatment for very cold freshwater (0.1-0.5 degrees C) in real ballast tank (750 m(3)) conditions aboard a ship and in large-volume (4.5 m(3)) polyethylene tanks. Concentrations of peracetic acid (PAA) and hydrogen peroxide (H2O2) gradually dropped by 41-59% over 5 days. The treatment altered the quality of the treated waters by causing a pH drop of 0.9-1.3 units and a fourfold to sevenfold increase in dissolved organic carbon and organophosphates concentrations. More than 90% of the biomass of free-floating micro-organisms and viable phytoplankton were eliminated within 48 h after treatment. The treatment resulted in 100% mortality in caged fish exposed to treated waters but was totally ineffective against adult zebra mussels and some nematods living in tank sediments. Toxic response from ecotoxicological assays indicated that treated waters after 5 days should be diluted by a factor of 1:2 to 1:200 to reduce toxicity below selected endpoints of acute lethality tests. On the basis of PAA degradation rate, fresh waters treated with 100-ppm PERACLEAN Ocean should be kept in ballast tanks for 15-20 days after treatment to reduce toxicity. It is concluded that the treatment can be an effective biocide to rapidly eliminate organisms of the water column inside the ballast tanks over a wide range of environmental conditions, but that the discharge of the toxic treated waters should be properly managed to minimize potential environmental impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".