Vector control reduces the rate of species invasion in the world's largest freshwater ecosystem
Bibliographic record
Abstract
Abstract The Great Lakes‐St Lawrence River basin is the world's most invaded freshwater system. Ballast water release from transoceanic shipping is deemed responsible for 65% of invasions in the basin since the modern St. Lawrence Seaway opened in 1959. Regulations requiring mid‐ocean exchange of ballast water applied in 1993 failed to stem ship‐mediated invasions because the procedure was not mandated for all ships. In 2006 and 2008, Canada and the United States, respectively, mandated that all transoceanic ships should conduct open ocean flushing to ensure that partially filled ballast tanks intended for discharge into the Great Lakes contained water of salinity ≥30 ppt before entering the Seaway. These regulations have been strictly enforced through record inspections and tests of ballast tank salinities of inbound ships. Before‐and‐after comparisons of total organismal abundance and species richness in ballast tanks revealed a substantial reduction in invasion risk from ships that conducted saltwater flushing. Since 2006, the rate of discovery of newly established non‐native species in the Great Lakes declined by 85% to its lowest level in two centuries. While multiple factors could plausibly contribute to this decline, empirical evidence supports the 2006/2008 ballast water regulation as the primary cause, highlighting the benefit of internationally coordinated vector control.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".