Seeding rate and cultivar effects on yield, yield components and grain quality of spring spelt in eastern Canada
Bibliographic record
Abstract
Dorval, I., Vanasse, A., Pageau, D. and Dion, Y. 2015. Seeding rate and cultivar effects on yield, yield components and grain quality of spring spelt in eastern Canada. Can. J. Plant. Sci. 95: 841-849. There is currently an increasing demand from master millers for spelt (Triticum aestivum ssp. spelta), but little is known about crop management of spring spelt under the eastern Canadian climate in organic or low-input systems. Field experiments were carried out at three sites in Quebec from 2011 to 2013 to assess the effect of cultivar (CDC Origin, CDC Zorba, CDC Nexon, CDC Silex) and seeding rate (250, 300, 350, 400 and 450 grains m-2) on grain and straw yields, yield components and some grain quality characteristics of spelt. CDC Origin produced higher hulled grain yields at all sites, while CDC Silex produced similar hulled grain yields and achieved the highest naked grain yields at two of the three test sites. The hull content varied from 24.0 to 37.6% among cultivars. CDC Origin had the highest hull content at each site. The seeding rate generally had no effect on yields, but had an effect on yield components; increasing seeding rates slightly increased the number of spikes per square metre and decreased the number of grains per spike, while the 1000-grain weight remained unaffected. Protein content of all cultivars was high (14.2 to 15.4%), while falling number varied from 219 to 385 s.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".