Multidimensional Approach to Invasive Species Prevention
Bibliographic record
Abstract
Nonindigenous species (NIS) cause global biotic homogenization and extinctions, with commercial shipping being a leading vector for spread of aquatic NIS. To reduce transport of NIS by ships, regulations requiring ballast water exchange (BWE) have been implemented by numerous countries. BWE appears to effectively reduce risk for freshwater ports, but provides only moderate protection of marine ports. In the near future, ships may be required to undertake ballast water treatment (BWT) to meet numeric performance standards, and BWE may be phased out of use. However, there are concerns that BWT systems may not operate reliably in fresh or turbid water, or both. Consequently, it has been proposed that BWE could be used in combination with BWT to maximize the positive benefits of both management strategies for protection of freshwater ports. We compared the biological efficacy of "BWE plus BWT" against "BWT alone" at a ballast water treatment experimental test facility. Our comparative evaluation showed that even though BWT alone significantly reduced abundances of all tested organism groups except total heterotrophic bacteria, the BWE plus BWT strategy significantly reduced abundances for all groups and furthermore resulted in significantly lower abundances of most groups when compared to BWT alone. Our study clearly demonstrates potential benefits of combining BWE with BWT to reduce invasion risk of freshwater organisms transported in ships' ballast water, and it should be of interest to policy makers and environmental managers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".