Diapausing zooplankton eggs remain viable despite exposure to open-ocean ballast water exchange: evidence from in situ exposure experiments
Bibliographic record
Abstract
To reduce the transfer of nonindigenous species, regulations require transoceanic ships to exchange ballast with ocean water before discharging into the Great Lakes. Although ballast water exchange (BWE) is effective for live freshwater animals, laboratory experiments provide mixed results with regards to its impact on diapausing zooplankton eggs. We conducted an in situ test of the effectiveness of BWE for treating diapausing eggs in ballast sediments. Incubation chambers containing ballast sediment were placed in ballast tanks of cargo vessels transiting from North America to Europe. Each vessel had paired ballast tanks, one of which remained filled with Great Lakes water (control), while the second was exchanged with mid-ocean water. Laboratory viability tests were then conducted to compare viability of eggs recovered from sediments placed in both treatments, as well as identical sediments that remained at the laboratory in cold storage. No significant differences in egg viability were detected between treatments, but more species hatched from sediment that remained in cold storage. Results indicate that physical conditions in ballast tanks may affect egg viability, but saltwater exposure does not eliminate the risk of species introductions via diapausing eggs. Strategies that minimize sediment accumulation in ballast tanks can reduce the risk of species introductions via diapausing eggs.
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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) | 0.000 | 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) | 0.001 | 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".