Yield Adjustment by Canola Grown at Different Plant Populations under Semiarid Conditions
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it