White Bean and Weed Desiccation With Pelargonic Acid
Bibliographic record
Abstract
Few studies have evaluated pelargonic acid for desiccating white bean and annual weeds. Five field experiments were conducted from 2021 to 2023 near Exeter and Ridgetown, Ontario, Canada to assess pelargonic acid applied alone or sequentially at different rates and several alternative herbicides for desiccating white bean and annual weeds. Pelargonic acid applied at 3400 g ai ha-1 desiccated white bean 88, 92, and 98% at 5, 8, and 14 days after application (DAA), respectively. Increasing the application rate, using sequential application, or adding 28% urea ammonium nitrate (UAN), did not improve white bean desiccation. Pelargonic acid applied at 3400 g ai ha-1 desiccated green pigweed 0, 2, and 1%; common ragweed 4, 3, and 3%; common lambsquarters 0, 0, and 0%; green foxtail 8, 12, and 15%; and barnyardgrass 5, 5, and 4% at 5, 8, and 14 DAA, respectively. Increasing the application rate, using sequential applications, or adding 28% UAN did not improve overall weed desiccation, except for green foxtail where desiccation was enhanced 14, 14, and 15% with pelargonic acid applied at 6800 g ai ha-1 at 5, 8, and 14 DAA, respectively and common ragweed where desiccation was increased 5% with pelargonic acid applied at 6800 g ai ha-1 at 8 DAA. At 14 DAA, green pigweed, common ragweed, common lambsquarters, green foxtail and barnyardgrass desiccation with carfentrazone-ethyl was 30, 6, 0, 11, and 7%; with ammonium salt of fatty acid was with 60, 12, 0, 59, and 36%; with flumioxazin was 54, 68, 31, 90, and 69%; with saflufenacil was 100, 96, 75, 37, and 55%; with tiafenacil was 100, 88, 5, 80, and 42%; with glufosinate was 100, 95, 100, 99, and 96%; and with diquat was 100, 99, 99, 99, and 96%, respectively. Dry bean desiccation with ammonium salt of fatty acid, glufosinate, carfentrazone-ethyl, flumioxazin, saflufenacil, tiafenacil, and diquat was 98-99% at 14 DAA. Among the desiccants evaluated, diquat and glufosinate were the most effective with 99% white bean desiccation and 95-100% desiccation of annual weeds at 14 DAA.
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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 | 0.000 | 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) | 0.001 | 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".