Narrow Rows and Residue Management Increase Seed Yield of Three Turfgrasses
Bibliographic record
Abstract
Trials were seeded in 1993 at Saskatoon SK and Brooks AB, Canada and assessed in 1994 and 1995 to examine the impact of residue management, row spacing and seeding rate on seed yields of Kentucky bluegrass, creeping bentgrass, and creeping red fescue, with the focus primarily on Kentucky bluegrass. The highest, most consistent yields were achieved in the first production year, and yields were generally highest at narrow (20 - 40 cm) row spacings at that time. Without aggressive residue management such as burning or close mowing (scalping), yield of all three species declined dramatically in the second harvest year (less pronounced at wide row spacing). Aggressive management consistently produced higher yields than mowing or baling, but even the best yields were lower than those in the first harvest. Seeding rate did not have a consistent effect on Kentucky bluegrass seed yield, and residue management did not affect the incidence of silvertop.
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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".