<i>Metarhizium anisopliae</i> Seed Treatment Increases Yield of Field Corn When Applied for Wireworm Control
Bibliographic record
Abstract
In an effort to protect field corn (Zea mays L.) from wireworm (Agriotes obscurus L.) herbivory and yield loss, seeds were treated with conidia of Metarhizium anisopliae strain F52 alone or in combination with clothianidin or spinosad before planting at three farm fields in south coastal British Columbia, Canada. Corn seed treated with M. anisopliae conidia (main effect) resulted in significant increases in stand density (78% M. anisopliae treated vs. 67% no M. anisopliae) and stock and foliage area fresh wt. yield (9.6 Mg ha−1 M. anisopliae treated vs. 7.6 Mg ha−1 no M. anisopliae), and significantly increased plant (stock and foliage) fresh wt. when it was applied together with spinosad or with no additional agrichemical at one location. Spinosad had no effect on corn yield, whereas clothianidin caused a significant increase in plant stand density and yield. Wireworm cadavers showing M. anisopliae strain F52 growth were retrieved from treated plots, suggesting that the increase in yield may have been due to wireworm control. Laboratory experiments provided no evidence that the increase in stand density and yield from the M. anisopliae‐treated corn seed was attributable to an increase in germination rate or root growth. We concluded that seed treatment with this fungus may be a novel method to increase stand density and yield of corn.
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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".