An environmental weed risk assessment model for Australian forage improvement programs
Bibliographic record
Abstract
Many plant species with agronomic potential have been introduced for livestock forage and have subsequently become weeds of natural ecosystems, or ‘environmental weeds’. Stringent border quarantine procedures introduced by Australia in 1997 ensure few high weed risk species are now imported into the country; however, there are no protocols for assessing and managing weed risk in use on a national scale ‘post-border’ (i.e. once a plant species is in the country). Environmental weed risk management in forage improvement programs aims to minimise the risk that new species and cultivar introductions will be invasive in natural ecosystems. We describe an environmental weed risk assessment (EWRA) model specifically aimed at assessing the weed potential of exotic and native forage species. The EWRA model predicts and ranks species for weed risk by assessing invasiveness, impacts and potential distribution. Assessments are based on published evidence, experimental observations and intuitive responses from experienced pasture researchers, in collaboration with weed experts. This model specifically addresses the need for environmental weed risk management in forage improvement programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".