Combining Ballast Water Exchange and Treatment To Maximize Prevention of Species Introductions to Freshwater Ecosystems
Bibliographic record
Abstract
The most effective way to manage species transfers is to prevent their introduction via vector regulation. Soon, international ships will be required to meet numeric ballast discharge standards using ballast water treatment (BWT) systems, and ballast water exchange (BWE), currently required by several countries, will be phased out. However, there are concerns that BWT systems may not function reliably in fresh and/or turbid water. A land-based evaluation of simulated "BWE plus BWT" versus "BWT alone" demonstrated potential benefits of combining BWE with BWT for protection of freshwater ecosystems. We conducted ship-based testing to compare the efficacy of "BWE plus BWT" versus "BWT alone" on voyages starting with freshwater ballast. We tested the hypotheses that there is an additional effect of "BWE plus BWT" compared to "BWT alone" on the reduction of plankton, and that taxa remaining after "BWE plus BWT" will be marine (low risk for establishment at freshwater recipient ports). Our study found that BWE has significant additional effect on the reduction of plankton, and this effect increases with initial abundance. As per expectations, "BWT alone" tanks contained higher risk freshwater or euryhaline taxa at discharge, while "BWE plus BWT" tanks contained mostly lower risk marine taxa unlikely to survive in recipient freshwater ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".