Effect of Biostimulants Added to Postemergence Herbicides in Corn, Oats and Winter Wheat
Bibliographic record
Abstract
There is limited information available on the effect of biostimulants such as Crop Booster or RR SoyBooster on corn, oats and winter wheat under Ontario environmental conditions. A total of 37 field experiments were conducted in corn, oats and winter wheat at two locations (Ridgetown and Exeter, Ontario, Canada) to evaluate the effect of Crop Booster or RR SoyBooster on crop injury, weed control and yield. The addition of Crop Booster to glyphosate did not affect weed control or corn yield except at 4 weeks after herbicide application (WAA) when control of pigweed species was increased by 1% and at 4 and 8 WAA when control of common lambsquarters was reduced by 1%. The addition of RR SoyBooster to glyphosate did not affect crop injury, weed control or corn yield. The addition of Crop Booster to glyphosate + topramezone + atrazine did not affect crop injury, weed control or corn yield except at 4 WAA when control of common ragweed was reduced by 1%. The tank mix of Crop Booster with glyphosate + thiencarbazone-methyl did not affect crop injury, weed control or corn yield except at 4 WAA when control of green foxtail and annual grasses were reduced by 2% and 1%, respectively. The addition of Crop Booster to bromoxynil/ MCPA had no significant effect on crop injury, weed control or yield of oats or winter wheat.
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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".