Ballast-mediated animal introductions in the Laurentian Great Lakes: retrospective and prospective analyses
Bibliographic record
Abstract
Since completion of the St. Lawrence Seaway in 1959, at least 43 nonindigenous species (NIS) of animals and protists have established in the Laurentian Great Lakes, of which ~67% were attributed to discharge of ballast water from commercial ships. Twenty-three NIS were first discovered in four "hotspot" areas with a high representation of NIS, most notably the Lake Huron Lake Erie corridor. Despite implementation of the voluntary (1989, Canada) and mandatory (1993, U.S.A.) ballast water exchange (BWE) regulations, NIS were discovered at a higher rate during the 1990s than in the preceding three decades. Here we integrate knowledge of species' invasion histories, shipping traffic patterns, and physicochemical factors that constrain species' survivorship during ballast-mediated transfer to assess the risk of future introductions to the Great Lakes. Our risk-assessment model identified 26 high-risk species that are likely to survive intercontinental transfer in ballast tanks. Of these, 10 species have already invaded the Great Lakes. An additional 37 lower-risk species, of which six have already invaded, show some but not all attributes needed for successful introduction under current BWE management. Our model indicates that the Great Lakes remain vulnerable to ship-mediated NIS invasions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".