Investigating the links between trifluralin persistence in Western Australian soils and trifluralin resistance in resident annual ryegrass ( <i>Lolium rigidum</i> ) populations
Bibliographic record
Abstract
Abstract The pre-emergence herbicide trifluralin is widely used in the minimum-tillage cropping systems of Australia, with the result that resistance to trifluralin is increasing in the major weed of the region, annual ryegrass ( Lolium rigidum ). Repeated exposure to low herbicide rates is also known to result in the rapid evolution of resistance in weed populations. As trifluralin is highly volatile, readily photo-decomposed, metabolised by soil microbes and to bind strongly to soil organic matter, there are many factors that could result in weed populations receiving reduced (even sub-lethal) rates of the herbicide. To investigate whether trifluralin dissipation could play a role in the increasing levels of trifluralin resistance in annual ryegrass, resistance levels of populations from 18 Western Australian farms were compared with the dissipation rate of trifluralin applied to soil collected from these farms. Although there was no direct correlation between resistance level and trifluralin half-life, there were links between resistance and soil properties which suggest that higher rates of trifluralin dissipation could make a minor contribution to the development of resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".