Frost seeding increases spring cereal yield
Bibliographic record
Abstract
Short growing season and mid-summer heat and drought are limiting factors for spring cereal production in Canada, suggesting that higher and more stable yields may be possible if the seeding date occurred earlier in the spring. Field trials were conducted in southern Ontario in 2003 and 2004 to compare development and yield potential of frost (early April) and conventional (late April–early May) seeded hard red spring wheat (Triticum aestivum L.), spring barley (Hordeum vulgare L.), and oat (Avena sativa L.) established using commercially available no-till planting equipment. Frost seeding had lower plant populations than conventional seeding, with pre-tillering plant population reductions for frost seeding averaging 44 plants m−2 (12%) for wheat and 27 plants m−2 (10%) for oats. In spite of lower plant population, frost seeding yields were higher than conventional seeding, with yield increases averaging 0.66 Mg ha−1 (24%) for wheat, 0.72 Mg ha−1 (20%) for oats, and 0.36 Mg ha−1 (11%, 2004 only) for barley. Frost seeded cereals had earlier occurrence of key phenological stages with average heading dates for frost seeded wheat and barley occurring 5 d earlier. Frost seeded cereals also had a longer vegetative period, which, along with earlier heading dates, contributed to increased yields for frost seeded cereals.
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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) | 0.002 | 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".