Which calibrated threshold is appropriate for ranking non-native species using scores generated by WRA-type screening toolkits that assess risks under both current and future climate conditions?
Bibliographic record
Abstract
Score-based decision-support tools are increasingly used to identify potentially invasive non-native species as part of the risk screening (initial risk identification) component of non-native species risk analysis. Amongst these tools are the Weed Risk Assessment (WRA) and its derivatives, e.g. the Aquatic Species Invasiveness Screening Kit (AS-ISK), which have been extensively used on a large variety of terrestrial and aquatic plants and of aquatic animals worldwide. In this paper, a correction is made to the previous guidance on the use of two separate thresholds to risk-rank species, i.e. one for current climate conditions (basic risk assessment: BRA threshold) and one for future climate conditions (BRA + climate change assessment: BRA+CCA threshold). Re-evaluation of this practice reveals that, to avoid the incorrect risk-ranking of species, only the BRA threshold should be used in all future applications of WRAtype toolkits that include a separate set of climate-change questions -at present, this involves the AS-ISK and the newly released Terrestrial Animal Species Invasiveness Screening Kit (TAS-ISK). As a result of this revised guidance, all published studies containing AS-ISK applications to date are reviewed here, and where approrpiate corrected risk ranks are provided for species that were risk-ranked using a BRA+CCA threshold. Corrections are also made whenever applicable to published errors or incorrect risk ranks based on the BRA threshold in the AS-ISK applications reviewed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.010 | 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".