Relative importance of vessel hull fouling and ballast water as transport vectors of nonindigenous species to the Canadian Arctic
Bibliographic record
Abstract
Ships’ hull fouling and ballast water are leading vectors of marine nonindigenous species globally, yet few studies have examined their magnitude in the Arctic. To determine the relative importance of these vectors in Canada’s Arctic, we collected hull and ballast water samples from 13 and 32 vessels, respectively, at Churchill, Manitoba. We compared total abundance and richness of invertebrates transported on hulls versus those in ballast water. We found that hull fouling was associated with higher total abundance and richness of nonindigenous species when compared with ballast water. Additionally, a significant positive richness–total abundance relationship for nonindigenous species for hull fouling but not for ballast water assemblages suggests that the likelihood of a high-risk (i.e., species-rich and high abundance) introduction event is greater for the former than the latter vector. The discovery of viable, widespread nonindigenous barnacles in hull samples further underscores the prominence of hull fouling over ballast water as a vector of nonindigenous species. Our study demonstrates that hull fouling is a more important vector for transfer of nonindigenous species to the Canadian Arctic than ballast water based on abundance and richness of nonindigenous species transported by the two vectors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".