Effect of Method of Application, Herbicide Rate and Cultivar on Processing Pea Tolerance to Saflufenacil
Bibliographic record
Abstract
The purpose of this work was to determine the effect of method of application, herbicide rate and cultivar on tolerance of processing pea tolerance to saflufenacil. Two field experiments were established to address this—each experiment was conducted over a 3-year period. The first experiment, conducted in 2014, 2015 and 2016, was arranged in a split-plot design with method of application (pre-plant incorporation (PPI) or preemergence (PRE)) as the main plot factor, and saflufenacil rate (0, 75 and 150 g ai ha-1) as the subplot factor. Pea (Pisum sativum L.) was not injured, and dry matter, pea tenderness and yield were not less than the untreated check when saflufenacil was applied either PPI or PRE, at 75 and 150 g ai ha-1 of the herbicide. The second experiment was conducted from 2017 to 2019, at two locations each year; each repetition of this experiment was arranged in a factorial design to determine the effect of two factors on processing pea: saflufenacil rate (0, 75 and 150 g ai ha-1) and cultivar. Saflufenacil did not cause more than 5% visible injury to pea, nor did it reduce pea dry matter, tenderness or marketable yield of the eight cultivars included in the experiment. Application method, saflufenacil rate and cultivar did not affect pea tolerance across a wide range of soil and environmental conditions. Registration of saflufenacil in processing pea would significantly improve growers’ options for control of Group 2 resistant broadleaf weeds such as common lamb’s-quarters (Chenopodium album L.), eastern black nightshade (Solanum ptycanthum Dunal.) and common ragweed (Ambrosia artemisiifolia L.).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".