Timely Autumn Seeding of Annual Ryegrass Is Essential for High Yield
Bibliographic record
Abstract
The use of annual ryegrass (Lolium multiflorum Lam.) as a winter cover crop and grazing option in the Southeast Unit-ed States has become a common practice. Recent research evaluating the effects of seeding time on seed yield in Canada determined autumn seeding produces the most desired results relative spring seeding, but indicated that varied autumn seeding rates would further their findings (Coulman et al. 2013). A University of Arkansas study utilized cool season annuals, wheat (Triticum aestivum L.) and annual ryegrass, to evaluate animal performance and seeding date effects. This research indicated that seeding cool-season annuals in early September may result in greater autumn forage production relative late October seeding. (Coffey et al. 2013). While current recommendations in the Southeast United States are to plant annual ryegrass in early autumn, differences among dates and locations have not been evaluated to maximize yield and provide the best forage utilization for producers. The objective of the study is to develop extension recommendations for autumn date seeding of annual ryegrass for maximum seasonal yield potential.
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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 | 0.001 | 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 | 0.001 | 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) | 0.003 | 0.001 |
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".