Consistency of impact assessment protocols for non-native species
Bibliographic record
Abstract
Standardized tools are needed to identify and prioritize the most harmful non-native species (NNS). A plethora of assessment protocols have been developed to evaluate the current and potential impacts of non-native species, but consistency among them has received limited attention. To estimate the consistency across impact assessment protocols, 89 specialists in biological invasions used 11 protocols to screen 57 NNS (2614 assessments). We tested if the consistency in the impact scoring across assessors, quantified as the coefficient of variation (CV), was dependent on the characteristics of the protocol, the taxonomic group and the expertise of the assessor. Mean CV across assessors was 40%, with a maximum of 223%. CV was lower for protocols with a low number of score levels, which demanded high levels of expertise, and when the assessors had greater expertise on the assessed species. The similarity among protocols with respect to the final scores was higher when the protocols considered the same impact types. We conclude that all protocols led to considerable inconsistency among assessors. In order to improve consistency, we highlight the importance of selecting assessors with high expertise, providing clear guidelines and adequate training but also deriving final decisions collaboratively by consensus.
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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 | 0.183 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".