Determination of optimal sowing dates and densities of winter wheat under Quebec growing conditions
Bibliographic record
Abstract
The objective of this project was to determine the optimal sowing dates and densities for winter wheat to increase winter survival and yield under Quebec growing conditions. The trials were carried out from 2014 to 2016 at four sites, representing three cereal production zones (zones 1, 2, and 3). Three cultivars were assessed using four sowing dates and four seeding densities (250, 350, 450, and 550 seeds m−2). In the first year, the wheat at two of the four sites survived (82%–100%), and in the second year, all the sites showed good survival rates (69%–99%). In zone 2, winter survival was higher for the early sowing dates compared with later dates. Sowing date and seeding density had no effect on survival in zones 1 and 3. Maximum yields were attained with sowing dates from mid- to late September in zone 1; from early to mid-September in zone 2; and from mid-August to mid-September in zone 3. An increase in seeding density from 250 to 550 seeds m−2 led to an average yield gain of 9% in zones 2 and 3, but no gain in zone 1. Winter survival rates and yield differ between cultivars.
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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 | 0.001 | 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".