Effect of preharvest applications of glyphosate on the drying, yield and quality of canola
Bibliographic record
Abstract
Potential use of preharvest applications of glyphosate in canola (Brassica rapa L.) in western Canada both as a harvest aid and to control weeds prompted this study. Experiments were conducted from 1988 to 1990 at four locations in the region to document the effectiveness of preharvest applications of glyphosate to enhance canola seed and foliage drydown and to measure the effect of such applications on seed yield and quality. Glyphosate was applied in early August to early September at rates of 0.45, 0.90 and 1.70 kg acid equivalent ha−1 to canola with seed moisture contents ranging from 79 to 12%. Seed and foliage moisture drydown were not enhanced by glyphosate treatment. Glyphosate treatments generally had little or no effect on canola seed yields, seed weight, seed germination, green seed content or oil content except when applied when the pods were green and when seed moisture contents were high. Seed/pod moisture was not a reliable indicator of stage of canola development and thus, should not be used alone as an indicator of when preharvest applications of glyphosate can be applied to canola without decreasing seed yield and quality. Key words: Glyphosate, canola, Brassica rapa L., preharvest application, desiccation, seed quality
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 | 0.000 | 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) | 0.001 | 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".