Effectiveness of ballast water management systems in the Great Lakes based on a paired uptake-discharge sample design
Bibliographic record
Abstract
Abstract Ships’ ballast water is a major vector for aquatic non-native species, particularly in the Great Lakes and St. Lawrence River (GLSLR) region. Effective September 8, 2024, international ships must comply with an international convention limiting live organisms in discharged ballast water, typically by installing onboard ballast water management systems (BWMS). This study assessed BWMS effectiveness in the GLSLR using a paired uptake-discharge sample design (before and after treatment), to evaluate live organism abundance in the two regulated size classes, and plankton diversity, using microscopy and high-throughput sequencing. Samples from 10 ships (11 paired events) collected in 2019 and 2022 revealed uptake abundances of living organisms up to 107,600 organisms/m 3 for the large (≥ 50 μm) and 169 organisms/mL for the small (≥ 10 to < 50 µm) size classes, respectively. While all discharge samples met the small size class limit, four samples exceeded the large size class limit, with BWMS reducing abundances by at least 98%. High uptake abundance and BWMS filter mesh size were key predictors of organism abundance in discharge samples, highlighting potential challenges in meeting the discharge standard in eutrophic waters. BWMS also reduced plankton diversity in discharge samples compared to uptake samples, with molecular methods showing greater sensitivity than microscopy but potentially detecting DNA from both live and dead organisms. The findings underline the need for further research to address BWMS performance in different water qualities and to improve technology. Complementary use of microscopy and molecular methods offers a comprehensive approach to evaluating plankton diversity and BWMS effectiveness.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".