Influence of Seeding Depth and Seedbed Preparation on Establishment, Growth and Yield of Fibre Flax (<i>Linum usitatissimum</i> L.) in Eastern Canada
Bibliographic record
Abstract
Abstract Research was conducted at the Macdonald Campus of McGill University (Québec, Canada) at three sites in 1997 and one site in 1998 to determine the effects and interactions of seeding depth (0, 1, 2, 4 or 6 cm) and seedbed preparation (i.e. soil rolling): none, rolling before or rolling after seeding on fibre flax (cv. Ariane) establishment, growth and yield. Seedbed preparation had little impact on the parameters measured while seeding depth had a variable effect on plant density, plant height, stem diameter and retted straw yield. Seeding depths of 1–4 cm provided consistently good establishment, growth and yield results. In 1997, there was an interaction between seeding depth and seedbed preparation on plant height, branching ratio and retted straw yield, although results were generally variable and tended to be site‐specific. In 1998, there was an interaction between seeding depth and seedbed preparation on plant height and stem diameter prior to harvest, with the results varying for all seeding depth‐seedbed preparation treatment combinations except for the 2‐cm depth treatment. Rolling of the seedbed before seeding on lighter soils and at a depth of 2 cm on most soils can improve establishment, growth and yields of fibre flax under eastern Canadian growing conditions.
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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 | 0.001 |
| 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".