A framework for the development of defensible and fit-for-purpose priority lists for non-indigenous species
Bibliographic record
Abstract
Producing prioritised lists of current and prospective non-indigenous species with the potential to cause significant harm is essential for several reasons but these need to be transparent both in the methods used and their justifications.Despite their varied applications, the criteria and components used to generate priority lists for nonindigenous species are often not indicated and there are no universal guidelines for their development.As such, we conducted a literature review identifying the common elements of existing priority lists to develop a framework, including considerations, to guide the creation of future priority lists for non-indigenous species.This framework is organised around five elements: (1) Scoping, (2) Compilation of a Master List, (3) Primary Assessment, (4) Secondary Assessment, and (5) Listing.Using the framework and associated flowchart, end users identify the considerations most relevant to their objectives so as to ascertain the specific criteria and parameters required to develop priority lists that are defensible and fit-for-purpose.This framework is designed to be applicable for a variety of purposes, is taxa and ecosystem-independent, and when implemented in a transparent way, will improve clarity for interpretation of, and comparisons across, priority lists, resulting in more widespread acceptance of lists among end users and stakeholders.
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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 | 0.196 | 0.134 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".