Coastal ship traffic: a significant introduction vector for potentially harmful dinoflagellates in eastern Canada
Bibliographic record
Abstract
We examined the risk of introduction associated with potentially toxic or otherwise harmful algae (HA) or nonindigenous species (NIS) of dinoflagellates in ballast water from 63 commercial ships visiting ports of eastern Canada in 2007–2009. Ship categories included transoceanics undergoing ballast water exchange (BWE) and coastal ships with or without BWE. Of 159 species of dinoflagellates observed in Lugol-preserved samples, 15 were potential HA (six Dinophysis spp.) and 46 were NIS (including three HA). We found at least one species of HA in 81% of all ships examined, and maximum cell concentrations reached nearly 4000 cells·L –1 . Coastal nonexchanged tankers carried the greatest cell concentrations of HA. NIS dinoflagellates were found in 56% of ships, significantly more in ships with BWE. There was no evidence that ships with BWE contained significantly fewer taxa or lower concentrations of HA dinoflagellates, indicating that BWE is not efficient in controlling the introduction of these organisms. In fact, BWE promoted the transport of NIS dinoflagellates, possibly because of the wide distribution of several of these species. Coastal ship traffic is a significant introduction pathway for HA (ships with and without BWE) and NIS (ships with BWE) dinoflagellates in eastern Canada.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".