Enhancement of tolpyralate + atrazine efficacy with adjuvants
Bibliographic record
Abstract
Abstract Tolpyralate is commonly mixed with atrazine for improved control of common annual weed species in corn production systems in the United States and Canada. Weed control efficacy with this mixture is enhanced with the addition of methylated seed oil (MSO) Concentrate®; however, there is little information on the efficacy of tolpyralate + atrazine with other proprietary adjuvants. Therefore, four trials were conducted at field research sites in Ontario, Canada, to evaluate the efficacy of tolpyralate + atrazine when applied with six different commercially available adjuvants on four annual broadleaf and two annual grass weed species in corn. The adjuvants evaluated were MSO Concentrate®, Agral® 90, Assist® Oil Concentrate, Carrier®, LI 700®, and Merge®. A treatment of tolpyralate + atrazine applied with no adjuvant was also included in the study. For the control of velvetleaf and wild mustard, the adjuvants evaluated with tolpyralate + atrazine did not improve control. At 8 wk after application (WAA), the use of Agral® 90, Assist® Oil Concentrate, Carrier®, MSO Concentrate®, or Merge® with tolpyralate + atrazine provided similar or greater control of common ragweed than tolpyralate + atrazine applied with LI 700®. At 8 WAA, the adjuvants performed similarly with tolpyralate + atrazine for the control of common lambsquarters; however, LI 700® was the only adjuvant that did not improve control compared to tolpyralate + atrazine applied without an adjuvant. At 8 WAA, MSO Concentrate®, Carrier®, and Merge® improved control of barnyardgrass and foxtail species with tolpyralate + atrazine to a similar or greater level than Assist® Oil Concentrate, Agral® 90, and LI 700®. Overall, MSO Concentrate®, Carrier®, or Merge® should be added to tolpyralate + atrazine for control of the myriad of weed species interfering with corn production.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".