Early seedling growth and forage production of diploid and tetraploid crested wheatgrass and Russian wildrye cultivars
Bibliographic record
Abstract
Cultivars selected for improved seedling vigour may also differ in seedling growth and subsequent forage production. The objective of this project was to compare three cultivars of crested wheatgrass (CWG) and four cultivars of Russian wildrye (RWR) for seedling growth in a greenhouse (GH) trial and two field trials when seeded at 15, 30 and 45 mm depths. Cultivars were Goliath, Kirk and Parkway CWG and SCR39903, Swift, Tetracan, and Tom RWR. Seedling emergence, tiller number, and seedling biomass were determined at 28 d after seeding (DAS) in all three trials. In addition, forage dry matter (DM) yield was determined for 2 yr in the field trials. Small-seeded diploid Parkway crested wheatgrass had reduced emergence at 45 mm seeding depth compared with larger-seeded tetraploid Kirk and Goliath. Goliath had reduced tillering compared with the other two CWG cultivars. The emergence of Tetracan tetraploid RWR was greater at deeper seeding depth than diploid cultivars, SCR39902, Swift and Tom in Field Trial 2, but not in Field Trial 1 and the GH trial. Seedling tiller number of Tetracan RWR was less than that of the other three cultivars. Two-year total CWG forage DM yield in the field was best correlated to emergence. In contrast, 2-yr total RWR forage DM was best correlated to seedling tiller number in GH and Field Trial 2 despite the low tiller numbers at 28 d after seeding. Field emergence of Russian wildrye did not appear to be limiting to seedling establishment in contrast to previous reports. Selection in RWR should include seedling tiller number in combination with seedling emergence in order to improve both seedling vigour and forage productivity. Key words: Establishment, tiller, emergence, forage yields, seedling depth
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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) | 0.000 | 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".