Efficacy of Thiamethoxam to Suppress Soybean Aphid Populations in Minnesota Soybean
Bibliographic record
Abstract
Soybean aphid poses a serious threat to soybean production in the United States and Canada by reducing plant height, pod number, and yield. Since its introduction, foliar insecticides have been the most common method to control soybean aphid. Treatment of soybean seed with a systemic insecticide may provide producers with an alternative to foliar applied insecticides. Therefore, our objective was to evaluate the efficacy of a seed treatment to suppress soybean aphid populations in soybean. In 2003, 2004, and 2005 we conducted field studies and supportive laboratory and in‐field bioassays (2003 and 2004 only for bioassays) to assess mortality under controlled conditions. In the field, soybean aphid populations were assessed weekly by counting the total number of aphids per plant. In an excised‐leaf bioassay, aphid mortality persisted 23 to 35 days after planting. In an in‐field bioassay (2004 only), intact plants showed longer persistence of thiomethoxam and aphid mortality persisted 49 days after planting and mortality was significantly higher in the older leaves than in newly‐expanded leaves. In all years and under various aphid densities, thiamethoxam significantly reduced season‐long aphid pressure by 45.0 to 66.7%. However, thiamethoxam treatment did not significantly increase yield in three of four location‐years, which coincided with low aphid density in untreated control plots.
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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) | 0.000 | 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".