A protocol for screening potentially invasive non-native species using Weed Risk Assessment-type decision-support tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is increasing use worldwide of electronic decision-support tools to identify potentially invasive non-native species so as to inform policy and management decisions aimed at preventing or mitigating the environmental and socio-economic impacts of biological invasions. This study reviews the analytical approaches used to calibrate scores generated by the Weed Risk Assessment and subsequent adaptations thereof and provides a protocol for: (i) the identification of the assessor(s) who will carry out the screenings; (ii) the definition of the risk assessment area; (iii) the criteria for selection of the species for screening; and (iv) the a priori categorisation of the species into invasive or non-invasive necessary to compute the thresholds by which to distinguish between high-risk and medium-risk non-native species. This analytical approach represents an evidence-based and statistically robust means with which to inform decision-makers and stakeholders about policy and management of potentially invasive species and is expected to serve as a general reference of forthcoming screening applications of Weed Risk Assessment-type toolkits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 it