Insecticide resistance and implications for future aphid management in Australian grains and pastures: a review
Bibliographic record
Abstract
Aphids can cause substantial damage to cereals, oilseeds and legumes through direct feeding and through the transmission of plant pathogenic viruses. Aphid-resistant varieties are only available for a limited number of crops. In Australia, growers often use prophylactic sprays to control aphids, but this strategy can lead to non-target effects and the development of insecticide resistance. Insecticide resistance is a problem in one aphid pest of Australian grains in Australia, the green peach aphid (Myzus persicae). Molecular analyses of field-collected samples demonstrate that amplified E4 esterase resistance to organophosphate insecticides is widespread in Australian grains across Australia. Knockdown resistance to pyrethroids is less abundant, but has an increased frequency in areas with known frequent use of these insecticides. Modified acetylcholinesterase resistance to dimethyl carbamates, such as pirimicarb, has not been found in Australia, nor has resistance to imidacloprid. Australian grain growers should consider control options that are less likely to promote insecticide resistance, and have reduced impacts on natural enemies. Research is ongoing in Australia and overseas to provide new strategies for aphid management 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".