Integrated shrub management in semi-arid woodlands of eastern Australia: ground and aerial application of defoliant to shrubs regenerating after disturbance
Bibliographic record
Abstract
This paper describes experiments undertaken at several sites in semi-arid woodlands of eastern Australia to determine if chemicals applied either on the ground or from the air reduce the density of shrubs regenerating after disturbance. Ground-spraying of Roundup® in the autumn was more effective than spring application in defoliating shrubs, especially 2-year-old coppice growth. Spraying of Roundup with a hand-held boom at 0.5 up to 2.5 kg glyphosate/ha identified rates to be used for boom spraying. Aerial spraying experiments were then undertaken across several sites and involved several target species. The location of sufficiently large areas where shrub regeneration was of an optimum age (i.e. about 2–3 years) proved to be extremely difficult due to prevailing drought conditions precluding the use of prescribed fire as a preliminary treatment. Nonetheless in one experiment, young (1-year-old) regrowth of firebush (Senna pleurocarpa) exhibited increased sensitivity to Roundup with significant shoot mortality recorded after it had been applied at 0.5 kg glyphosate/ha. Aerial spraying based on an ultra-low volume application of 10 L/ha further enhanced cost-effectiveness on this occasion. Economic analyses structured around 20-year partial budgeting and determination of net present value (NPV) suggested a profitable return could be expected where treatment was based on Roundup applied at this threshold rate 2 years after a prescribed fire, especially when the rehabilitation costs were spread over an entire paddock that had been only partially sprayed. Finally, operational aspects involving aerial spraying in these semi-arid woodlands are also discussed.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".