Saflufenacil Carryover Injury Varies among Rotational Crops
Bibliographic record
Abstract
Trials were established in 2007, 2008, and 2009 in Ontario, Canada, to determine the effect of soil residues of saflufenacil on growth, yield, and quality of eight rotational crops planted 1 yr after application. In the year of establishment, saflufenacil was applied PRE to field corn at rates of 75, 100, and 200 g ai ha−1. Cabbage, carrot, cucumber, onion, pea, pepper, potato, and sugar beet were planted 1 yr later, maintained weed-free, and plant dry weight, yield, and quality measures of interest to processors for each crop were determined. Reductions in dry weight and yield of all grades of cucumber were determined at both the 100 and 200 g ha−1rates of saflufenacil. Plant dry weight, bulb number, and size and yield of onion were also reduced by saflufenacil at 100 and 200 g ha−1. Sugar beet plant dry weight and yield, but not sucrose content, were decreased by saflufenacil at 100 and 200 g ha−1. Cabbage plant dry weight, head size, and yield; carrot root weight and yield; and pepper dry weight, fruit number and size, and yield were only reduced in those treatments in which twice the field corn rate had been applied to simulate the effect of spray overlap in the previous year. Pea and potato were not negatively impacted by applications of saflufenacil in the year prior to planting. It is recommended that cabbage, carrot, cucumber, onion, pepper, and sugar beet not be planted the year after saflufenacil application at rates up to 200 g ha−1. Pea and potato can be safely planted the year following application of saflufenacil up to rates of 200 g ha−1.
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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 | 0.001 | 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".