The effects of roller crimping direction for termination of fall sown cereal rye (<i>Secale cereale</i> L.)
Bibliographic record
Abstract
Consensus around the optimal direction of roller crimping a row-planted cover crop has not been established. Several publications report crimping either parallel or perpendicular to the direction of cover crop planting with little to no justification apart from unpublished observations or hypotheses. This study explicitly compared the effects of roller crimping direction on crimping efficacy, weed suppression, and cash crop yield. At Elora, ON in 2020 and 2021, and Harrow, ON in 2021, a cereal rye ( Secale cereale L.) cover crop was planted in three orientations (north-south vs. east-west vs. no rye control) then terminated with a roller crimper parallel or perpendicular to the direction of planting. A sweet corn ( Zea mays L.) cash crop was planted either north–south or east–west in the same direction as roller crimping. A split plot treatment of weediness (weedy vs. weed-free) was applied. It was found that roller crimping direction did not have a consistent effect on rye mortality or number of upright tillers, nor did it affect weed control. However, total marketable sweet corn fresh weight decreased in perpendicular crimped rye compared to parallel crimping, despite equivalent cob counts. We did not find evidence in this study to suggest that perpendicular roller crimping improves ground cover and therefore weed suppression, contrary to other unpublished observations. Given the effect on sweet corn yield, roller crimping perpendicular to the direction of cover crop planting may not be a suitable practice. Alternative methods for improving cover crop-based weed control should be investigated.
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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) | 0.001 | 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 | 0.001 |
| 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".