Yield Adjustment by Canola Grown at Different Plant Populations under Semiarid Conditions
Bibliographic record
Abstract
Establishing a good canola (rapeseed; Brassica napus L.) stand is difficult in the semiarid prairie region of Canada where low temperature, water stress, and soil crusting could result in poor seed bed conditions. A field study was conducted from 1999 to 2001 at Swift Current, SK, Canada, to determine the effect of a range of uniform (5 to 80 plants m−2) and nonuniform (seedlings from 1‐m lengths from two adjoining rows were removed and retained alternatively; 10 to 40 plants m−2) plant populations on yield and yield components of canola. Canola adjusted seed yield across a wide range of plant populations, although it did not compensate completely for the decreasing populations. Environmental conditions played a significant role in the expression of plasticity of canola. For example, in 2000, with slightly above‐normal growing season precipitation, canola maintained similar yield levels across a wide range of populations (20 to 80 plants m−2), while in 2001, with well below normal precipitation, seed yield declined as populations dropped below 40 plants m−2 Reducing plant population by half from 80 to 40 plants m−2 did not reduce seed yield when the reduced plant population was uniformly distributed, but reduced yield when the population was nonuniformly distributed. The primary response of canola to lower plant population was increased pods per plant through increased branching and increased pod retention at each node. The number of pods formed on primary and secondary branches increased as population decreased. Seeds per pod and seed weight were stable across populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".