Efficacy of Insecticides Against Spodoptera frugiperda (Smith, 1797)
Bibliographic record
Abstract
The Brazil’s economy is supported by agribusiness, however, the continuous cycle of food production and favorable climate contributes to the incidence of pest insects all year round. The fall armyworm Spodoptera frugiperda (Smith, 1797) (Lepidoptera: Noctuidae) is considered the main insect in the corn crop, due to voracity of the caterpillars and occurrence throughout the crop cycle. Therefore, the chemical control has been demanded considerably, causing emergence of populations resistant to the different products, as well as implications in the environment. Thus, studies are needed to evaluate the efficiency of chemical insecticide control according to the susceptibility and the stages of biological development of the pest. The objective of this work was to evaluate the performance of isolated and combined insecticides for the control of S. frugiperda caterpillars under laboratory conditions. In the residual and direct contact bioassays, 8 treatments with 5 replications were used in a completely randomized design, performed with 2nd and 5th instar. The results showed that by residual contact after 72 hours, clofenapir + zeta-cypermethrin treatment had 100% efficacy in the mortality of both instars. When applied via direct contact 72 hours later, the combined treatments showed an efficiency above 80%. However, for an integrated pest management program, where it was recommended the association of different control tactics, the management of insecticides with clofenapir + zeta-cypermethrin was effective with 100% control efficiency in both instars.
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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) | 0.001 | 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".