White Bean Desiccation With Caprylic Plus Capric Acid
Bibliographic record
Abstract
There is no published information on the efficacy of caprylic plus capric acid for desiccating weeds and white bean under Ontario environmental conditions. Five field experiments were conducted from 2021 to 2023 in southwestern Ontario, Canada to evaluate the effectiveness of caprylic plus capric acid, applied at 3, 6, and 9% v/v and other commonly used desiccant herbicides in white bean. White bean desiccation levels were generally similar to the nontreated control at most evaluation points, except at 6 and 9 days after application (DAA) with the application of caprylic plus capric acid at 6 and 9% v/v which resulted in up to 8 and 6% greater white bean desiccation, respectively, compared to the nontreated control. Caprylic plus capric acid at 3, 6, and 9% v/v desiccated green pigweed 0-51%, common lambsquarters 0-16%, common ragweed 4-21%, green foxtail 10-53%, and barnyardgrass 7-37%. Glyphosate, saflufenacil, and glyphosate + saflufenacil desiccated white bean by 80-99%, 81-100%, and 82-100%, respectively, at 2, 6, 9, and 15 DAA. These levels were generally similar to the nontreated control, except at 2 DAA, when white bean was desiccated 4% greater with glyphosate + saflufenacil than the nontreated control. At 15 DAA, glyphosate desiccated green pigweed 100%, common lambsquarters 100%, common ragweed 70%, green foxtail 96%, and barnyardgrass 82%; saflufenacil desiccated green pigweed 100%, common lambsquarters 69%, common ragweed 96%, green foxtail 41%; and barnyardgrass 28%; and glyphosate + saflufenacil desiccated green pigweed 100%, common lambsquarters 100%; common ragweed 99%, green foxtail 98%, and barnyardgrass 93%. Based on these findings, caprylic plus capric acid has limited potential as a desiccant for white bean.
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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".