Fermented or Floral? Developing a Generalized Food Bait Lure to Monitor Cutworm and Armyworm Moths (Lepidoptera: Noctuidae) in Field Crops
Bibliographic record
Abstract
Cutworms and armyworms (Lepidoptera: Noctuidae) are a pest complex in North America that cause sporadic damage in field crops on the Canadian Prairies; however, no methods have been developed to reliably monitor population densities. Food-based semiochemicals attract both sexes of adult moths and could be used to monitor multiple species with a single lure in a single trap. Here, we focus on enhancing the attractiveness of acetic acid and 3-methyl-1-butanol (AAMB) lures to redbacked cutworm (Euxoa ochrogaster) (RBC) and other noctuid pests. Experiments conducted in canola and wheat fields tested AAMB lures at different release rates, from different devices and in combination with other semiochemicals. High-release lures captured more females in canola, while low-release lures captured more males in wheat. Thus, crop volatiles may influence response to lures. Semiochemicals embedded in an inert matrix caught more RBC moths than semiochemicals released from Nalgene or polyethylene dispensers did. More RBC females were attracted to AAMB lures with 2-methyl-1-propanol than phenylacetaldehyde. Fermented volatiles appear to be a more reliable attractant than floral volatiles for these species. RBC moth antennae produced significant responses to all doses of phenylacetaldehyde tested in electroantennogram assays, but only to higher doses of acetic acid and 3-methyl-1-butanol. Physiological state of the RBC moths also influenced responsiveness to the tested semiochemical. Feeding status did not influence the antennal response to acetic acid and phenylacetaldehyde in either sex, but it increased the response to 3-methyl-1-butanol in females when fed. AAMB lures should be further developed to monitor RBC moths and other noctuid pests in field crops.
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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".