Comparison of pheromone trap design and lures for <i>Spodoptera frugiperda</i> in Togo and genetic characterization of moths caught
Bibliographic record
Abstract
Abstract Fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), is a pest of grain and vegetable crops endemic to the Western Hemisphere that has recently become widespread in sub‐Saharan Africa and has appeared in India. An important tool for monitoring S. frugiperda in the USA is pheromone trapping, which would be of value for use with African populations. Field experiments were conducted in Togo (West Africa) to compare capture of male fall armyworm using three commercially available pheromone lures and three trap designs. The objectives were to identify optimum trap × lure combinations with respect to sensitivity, specificity, and cost. Almost 400 moths were captured during the experiment. Differences were found in the number of S. frugiperda moths captured in the various trap designs and with the three pheromone lures, and in the number of non‐target moths captured with each lure. The merits of each trap × lure combination are discussed with respect to use in Africa. A nearly equal number of COI‐ CS (161) and COI ‐RS (158) moths was captured with no differences found in COI marker proportions among traps or lures. However, the diagnostic rice strain marker Tpi was rarely found. Overall, the genetic characterization of the pheromone trap collections indicated a consistent distribution of genetic markers from 2016 to 2017, suggesting a population at or near equilibrium.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".