Identifying higher risk invaders to the Columbia Glaciated Freshwater Ecoregion using a new screening tool: the Non-Indigenous Species Screening Tool (NISST)
Bibliographic record
Abstract
To inform non-indigenous species management and policy decisions it is often necessary to have a prioritized list of species and screening tools frequently are used for this purpose.However, despite numerous tools available that typically evaluate aspects of the introduction, establishment, and impacts of potential invasive species, there are still gaps in the criteria used meaning that not all tools are fit-for-purpose.Further, incorporating uncertainty in a way useful to managers has proven problematic.This paper introduces the Non-Indigenous Species Screening Tool, which was developed to fill such gaps and address common limitations in previous tools for screening potentially invasive species.Using a series of questions organized into three separate modules examining steps in the invasion process combined with both ecological impacts and socioeconomic impacts, this tool provides a semiquantitative valuation of risk which explicitly incorporates uncertainty into the score.Further, recognizing the increasing importance of considering climate change when assessing invasion risk, this tool also incorporates a modifier for this.We applied this tool to both existing non-indigenous species and potential ones (N = 44 species) across different taxa (plant, invertebrate, and fish) for the Columbia Glaciated Freshwater Ecoregion using four assessors.The question scores across all species and assessors showed strong correlation and the tool was able to differentiate low to high-risk species across taxa for species that were both present and not yet present.This suggests this tool is not taxa specific and can easily be applied for a variety of purposes.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".