Early autumn soil disturbance decreases persistence of volunteer summer‐annual oilseed rape (<i>Brassica napus</i>)
Bibliographic record
Abstract
Summary Volunteer summer‐annual oilseed rape (sOSR; Brassica napus) is an ongoing concern in Canadian crop production. Large harvest seed losses and secondary dormancy in this species generate a persistent volunteer seedbank. Yield loss in subsequent crops, potential sOSR oil profile contamination and herbicide‐resistance trait introgression create a need for effective sOSR seedbank management. This field study evaluated the effects of timing and type of implement of post‐harvest soil disturbance and seeding a winter cereal on volunteer sOSR population persistence and demographic life‐stage transition rates at five locations in Manitoba, Canada. Following sOSR harvest and supplemental seed rain, seedbank densities ranged from 6770 to 15360 and 50 to 2610 seeds m−2 among sites in autumn and spring respectively. In contrast to European research on winter‐annual oilseed rape, early autumn soil disturbance, shortly after sOSR harvest, was the best strategy to decrease volunteer sOSR persistence (3% population persistence from autumn to spring, compared with 6% in zero tillage). Substantial autumn seedling recruitment (38% of the autumn seedbank) and subsequent winterkill contributed to lower population persistence. Soil disturbance in spring stimulated spring seedling recruitment compared with other disturbance timings (11% and 3% of the spring seedbank, respectively). The implement used for soil disturbance and seeding winter wheat (Triticum aestivum) had minimal effect on population persistence. This research showed that timing of post‐harvest soil disturbance should be utilised as an effective tactic to decrease population persistence of volunteer sOSR via stimulation of autumn seedling recruitment and concomitant winterkill.
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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 | 0.000 | 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".