What and where? Identifying high-risk aquatic invasive species and hotspots of suitable habitat in the Arctic
Bibliographic record
Abstract
The risk of aquatic invasive species (AIS) introductions in the Arctic is expected to increase with ongoing trends of greater shipping activity, resource exploitation, and climate warming in the region. We identified a suite of AIS (benthos, zooplankton and macroalgae) with the greatest likelihood of introduction and impact in the Canadian Arctic using the Canadian Marine Invasive Screening Tool. The top sixteen riskiest species (mainly benthic) were then modelled to predict the potential spatial distributions (habitat modelling using Maximum Entropy) at an Arctic scale. Modelling was conducted under present environmental conditions and under two future global warming scenarios (2050 and 2100). Results show that hotspots or regions where suitable habitat is more densely accumulated for modelled AIS are in the Hudson Complex, Chukchi / Eastern Bering Sea, and Barents / White Sea. Most taxonomic groups showed a trend for a positive poleward shift in the future, increasing from the present time to the end of the century. This approach will aid in the identification of present and future high-risk areas for AIS in response to global warming.
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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 | 0.001 |
| 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) | 0.001 | 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".