A Horizon Scan watchlist of freshwater non-indigenous species in the maritime provinces of Canada
Bibliographic record
Abstract
There is a growing in-flux of non-indigenous species (NIS) brought to new locations via anthropogenic pathways, some of which are harmful to ecosystems, economies, and social and cultural health of receiving environments.In order to protect native ecosystems from invasion by novel NIS, watchlists of potentially problematic species are required but this task can feel overwhelming when considering the vastness of global biodiversity.To date in Canada, the Great Lakes are the only jurisdiction to have generated a freshwater NIS watchlist and published the associated methods.Here we compiled a working list of 4,446 species (plants, invertebrates, vertebrates) which was refined to 221 species based on taxa of interest (i.e.freshwater species), climate matching, invasion status and history, and associated vector presence, to focus on species relevant to the Maritime Provinces of Canada (i.e.Nova Scotia, New Brunswick, and Prince Edward Island).Using a rapid-level risk assessment tool applied to the refined species list, we identified 130 NIS as high-risk invaders to the Maritimes.We also identified numerous species with knowledge gaps that require further research.Common gaps included species' ecological thresholds, summaries of direct and indirect ecosystem impacts, and socio-economic impacts of species.We also noted the vectors associated with each of the 221 species.Although this work is specific to the Maritimes, the methodology and species list (including risk assessments) could easily be adapted to other regions of Canada or elsewhere.This project represents the first Canadian-led multi-province, multi-taxon, large scale horizon scan aimed at identifying species for management action, including listing in legislation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".