A temporal assessment of risk of non-indigenous species introduction by ballast water to Canadian coastal waters based on environmental similarity
Bibliographic record
Abstract
The environmental similarity scores between source and recipient locations are essential in ballast water risk assessment (BWRA) models used to estimate the potential for non-indigenous species (NIS) introduction, survival, and establishment, and to guide management strategies aiming to minimize biodiversity loss and economic impacts. Previous BWRA models incorporate annual-scale environmental data, which may overlook seasonal variability. In this study, temporal variation in sea surface temperature and salinity data were examined at global ports, and the influence of this variation on environmental distance calculations (and corresponding risk of NIS) was examined for ballast water discharges in Canada by comparing outputs from monthly and annual scale assessments in a BWRA model. Except for some outliers in the Pacific region, the environmental distances based on monthly scale data generally become smaller in all regions, demonstrating that the model using annual decadal average environmental data to inform environmental matching can underestimate risk of NIS survival and establishment in comparison to monthly data. The results of this study suggest future evaluations incorporating the date of ballast water uptake and discharge can provide a more sensitive assessment of risk reflecting seasonal variability compared to an annual average risk model.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".