Chaff collection reduces seed dispersal of wild oat (<i>Avena fatua</i>) by a combine harvester
Bibliographic record
Abstract
Combine harvesters have the potential to disperse weed seeds great distances. Reducing this dispersal may be important in an integrated weed management system. The objectives of this study were to determine the distance that wild oat seeds are dispersed by a combine harvester and the effect of chaff collection on combine harvester seed dispersal. This was measured by sampling wild oat seeds at varying distances behind a combine equipped with a removable chaff collection system after it passed through a wild oat patch. Chaff collection consistently reduced the amount and distance that wild oat seeds were dispersed. This occurred because more than 74% of the total wild oat seed that were ejected from the combine were in the chaff. Because most of the chaff falls in a row directly behind the combine, chaff collection only affected dispersal in this area. In 1996, chaff collection reduced wild oat seed dispersal past the wild oat patch to less than 10 seeds m−2at 45 m, whereas without chaff collection, there was greater than 10 seeds m−2up to 145 m. At distances beyond 145 m, chaff collection had no significant effect on seed dispersal. Chaff collection may be an important tool in an integrated weed management program because it may slow weed invasions and reduce the expansion of weed patches.
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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 | 0.000 | 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".