Evaluation of panicle removal methods and crop topping applications as supplemental tools for wild oat (<i>Avena fatua</i>) management
Bibliographic record
Abstract
Abstract Increased frequency and occurrence of herbicide-resistant biotypes heightens the need for alternative wild oat management strategies. This study aimed to exploit the height differential between wild oat and crops by targeting wild oat between panicle emergence and seed shed timing. Two field studies were conducted either in Lacombe, AB, or Lacombe, AB and Saskatoon, SK, from 2015 to 2017. In the first study, we compared panicle removal methods: hand clipping, use of a hedge trimmer, and a selective herbicide crop topping application to a weedy check and an industry standard in-crop herbicide application in wheat. These treatments were tested early (at panicle emergence), late (at initiation of seed shed), or in combination at one location over 3 yr. In the second study, we investigated optimal timing of panicle removal via a hedge trimmer with weekly removals in comparison to a weedy check in wheat and lentil. This study was conducted at two locations, Lacombe, AB, and Saskatoon, SK, over 3 yr. Among all the tested methods, the early crop topping treatment consistently had the largest impact on wild oat density, dockage, seedbank, and subsequent year crop yield. The early (at panicle emergence) or combination of early and late (at initiation of seed shed) treatments tended to reduce wild oat populations the following season the most compared to the late treatments. Subsequent wild oat populations were not influenced by panicle removal timing, but only by crop and location interactions. Panicle removal timing did significantly affect wild oat dockage in the year of treatment, but no consistent optimal timing could be identified. However, the two studies together highlight additional questions to be investigated, as well as the opportunity to manage wild oat seedbank inputs at the panicle emergence stage of the wild oat lifecycle.
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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 | 0.001 | 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".