Managing invasive weeds under climate change: considering the current and potential future distribution of <i>Buddleja davidii</i>
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
Kriticos DJ, Watt MS, Potter KJB, Manning LK, Alexander NS & Tallent‐ Halsell N (2011). Managing invasive weeds under climate change: considering the current and potential future distribution of Buddleja davidii . Weed Research 51 , 85–96. Summary Buddleja davidii is both a prized garden ornamental and an invasive shrub that rapidly colonises disturbed ground. Originally from China, B. davidii has been widely distributed by horticulturalists and has subsequently invaded much of Europe and New Zealand, and to a lesser degree the Americas and Australia. The present and future climate suitability for B. davidii was assessed using a process‐oriented climate suitability model. There appears to be a considerable scope for further invasion, with the most suitable areas occurring adjacent to existing naturalised populations in the north‐eastern United States, north‐eastern Europe, south‐eastern Australia and south‐eastern New Zealand. Under future climates, the potential distribution and climate suitability for B. davidii increases most noticeably in the northern United States and southern Canada, northern and eastern Europe, and to a lesser extent in the south‐western part of the South Island of New Zealand. Elsewhere, there are projected poleward range shifts (South America) or range contractions out of subtropical areas (Africa and Australia). Climate‐based potential distribution models can help adapt weed management programmes to expected climate changes by: (i) classifying areas for the different types of weed management, (ii) supporting strategic control initiatives to prevent the spread of a weed, (iii) informing the reallocation of resources away from controlling a weed where climate suitability is expected to diminish in the future and (iv) identifying opportunities for relatively inexpensive preventative management to be applied to minimise future weed impacts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it