Value for money? Investment in weed management in Australian rangelands
Bibliographic record
Abstract
Increased awareness of the threat posed by non-native species to biodiversity and productivity has prompted an unprecedented commitment and investment in weed management activities throughout rangeland Australia. Since the launching of National Weeds Program in 1996 under the first phase of the Natural Heritage Trust (NHT), there has been a substantial increase in coordinated and strategic investment in weed management across the rangelands. Almost AU$25 million of Australian Government funding has been invested in projects specifically targeting Weeds of National Significance (WONS) that occur in the rangelands (14 species) and a further AU$56 million on projects conducted in the rangelands that included a weed management component. Substantial funding has also been invested by other levels of government, non-government organisations and landholders. We review this investment in relation to the level of funding, the types of weeds targeted, the range of projects undertaken and the effectiveness of these projects within Australia’s rangelands. Achievements include successful eradications, preventions, early interventions, containments, mitigation of impacts, increased awareness of weed threats and general capacity to respond to weed management issues. Our review highlights several areas that, if addressed, will result in a substantial increase in the effectiveness of weed management efforts. These include: addressing discrepancies between states/territories in terms of funding and commitment to weed management; resolving conflicts between stakeholders in relation to the cost-benefit of non-native pasture grasses; encouraging projects that consider the broader natural resource management context of weed infestations; encouraging projects that examine weed complexes or the impacts of weeds in habitats with high biodiversity values such as riparian zones; and detecting and controlling weeds in the early stages of establishment. Finally, the collection of baseline information and alignment of reporting schedules with the longer term benefits of weed management projects will allow an assessment of the effectiveness of weed management projects and more strategic allocation of resources in the future.
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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".