Antifouling Effectiveness and Potential Toxicological Risk of an Elastomer-Based Coating against Zebra Mussels
Bibliographic record
Abstract
The fouling of hard substrates by zebra mussels (Dreissena sp.) in freshwater ecosystems is a persistent problem which calls for antifouling treatments being fully efficient, long-lasting, and environmentally safe. The present study assessed the potential toxicological impact and the effectiveness of an elastomer-based coating containing salts that would make surfaces repulsive to zebra mussel attachment. Laboratory testing using standard analytical methods for water and wastewater, and a battery of six bioassays confirmed that the leachates from the coating exhibited no toxic response suggesting its safety to the receiving environment. In situ experiments using multi-plate collectors indicated that biofouling by zebra mussels and sponges on coated surfaces was effectively reduced by up to 97% over one growing season. Effectiveness of the coating was slightly less (82%) during the second growing season. Results suggested that the repulsive effect would be due to the presence of salts within the elastomer-based coating affecting both zebra mussels and other freshwater organisms. Saltwater experiments indicated that the coating was totally ineffective to prevent biofouling in marine environments. In addition of being environmentally safe to use, results suggested that the coating can be an effective antifouling product for submerged structures in freshwater environments.
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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".