Evaluating Seed Shatter of Economically Important Weed Species
Bibliographic record
Abstract
The increasing occurrence of herbicide resistance, along with no new herbicide modes of action developed in over 30 yr, have increased the need for nonherbicidal weed management strategies and tactics. Harvest weed seed control (HWSC) practices have been successfully adopted in Australia to manage problematic weeds. For HWSC to be effective, a high proportion of weed seeds must be retained on the plant at crop maturity. This 2-yr (2014, 2015) study evaluated seed shatter of wild oat, green foxtail, wild mustard, and cleavers in both an early (field pea) and late (spring wheat) maturity crop in field experiments at Scott, Saskatchewan. Seed shatter was assessed using shatter trays collected once a week during crop ripening stage, as well as at two crop maturation or harvest stages (swathing, direct-combining). Seed shatter differed among weed species, but was similar between crops at maturity: ca. 30% for wild oat, 5% for cleavers, < 2% for wild mustard, and < 1% for green foxtail. Overall, seed shatter of wild oat occurred sooner and at greater levels during the growing season compared with the other weed species. Viability of both shattered and plant-retained seeds was relatively high for all species. The small amount of seed shatter of cleavers, wild mustard, and green foxtail suggests that these species may be suitable candidates for HWSC. Due to the amount and timing of wild oat seed shatter, HWSC may not reduce population abundance of this grassy weed.
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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 | 0.001 | 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".