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 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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".