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.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".