Economic evaluation of seeding decisions in hybrid and open-pollinated herbicide-resistant canola (<i>Brassica napus</i>)
Bibliographic record
Abstract
Herbicide-resistant canola (Brassica napus) is widely cultivated in western Canada. Data from a multi-year field experiment conducted at three locations in Alberta were used to investigate the effect of canola cultivar, time of seeding, polymer seed coating (PSC) on profitability and the net return risk. Cultivars included hybrid and open pollinated. Time of seeding was early and late fall, and early and normal spring. The hybrid canola generally had higher mean net return than the open-pollinated cultivar. The probability of thin canola stand was higher for fall dormant seeding compared with spring seeding. The use of PSC for early fall dormant seeding reduced the likelihood of thin canola stand, which translated into risk-efficient decision. However, PSC use for late fall seeding had mixed results. Early and normal spring seeding was generally risk-efficient at all locations. In exceptional cases, net returns from late fall dormant seeding (with or without PSC) were similar to that of spring seeding. Re-seeding of fall seeded canola in the spring could be beneficial, if the plant density was below 20 plant m-2. Seeding decisions were robust across a wide range of canola prices, based on the probability of negative net returns. Key words: Canola, fall seeding, net returns, polymer seed coat, re-seeding, risk
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 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".