Evaluation of a new screening-level risk assessment tool applied to non-indigenous marine invertebrates in Canadian coastal waters
Bibliographic record
Abstract
Screening-level risk assessment tools for non-indigenous species are useful to inform management and policy decisions. While a number of tools have been developed to evaluate the risk of introductions in terrestrial and freshwater environments, only one tool is available for marine invertebrates [Marine Invertebrate Invasiveness Scoring Kit (MI-ISK)] and it has not been calibrated or evaluated previously. This paper introduces the Canadian Marine Invasive Screening Tool (CMIST), a tool based on a series of questions related to the sequence of events in the invasion process. We assessed the performance of both CMIST and MI-ISK using 60 species/ecosystem combinations of non-indigenous marine invertebrate species known to have been introduced to three Canadian marine ecoregions. Assessment scores were compared to the results of an expert opinion survey about the level of ecological risk posed by these species in these ecoregions. In addition, a simple way to quantify uncertainty and compute confidence limits surrounding expert evaluations and CMIST scores is presented; the technique could be incorporated into other existing tools. The risk scores returned by the two tools generally were similar, had comparable levels of inter-assessor variability, and correlated well with the results of the expert opinion survey for two of the three ecoregions. In the third ecoregion, we believe the lack of highly problematic species and greater uncertainty (linked to less reliable information) contributed to the weaker correlation. For all ecoregions, CMIST scores were more strongly correlated with expert opinion scores when compared to MI-ISK scores. Finally, we demonstrate how CMIST can be used for new incursions; for each ecoregion we scored 15 additional species not yet introduced and compared these scores with those of species already present. Overall CMIST is a promising screening-level tool; it distinguishes between elements of invasion risk (likelihood and impact), has fewer questions to score than similar tools, and could easily be adapted for other taxa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".