Monitoring the diversity of pest and nonpest noctuid moth (Lepidoptera: Noctuidae) species in Canadian prairie agroecosystems
Bibliographic record
Abstract
Abstract The Noctuidae (Lepidoptera) comprise the most diverse and abundant lepidopteran families in the Canadian Prairie Ecozone. Within this group, some species are agricultural pests that require monitoring. Pheromone lures target specific species, whereas food-bait lures attract a broader range. This study reports the diversity and abundance of noctuid moths captured in traps baited with female sex pheromones of pest species and with food-bait lures consisting of acetic acid and 3-methyl-1-butanol (AAMB) with fermented byproduct or floral volatile compounds. Food-bait lures that attract pests and nonpest species can provide insight into moth populations and species richness in human-managed ecosystems. We trapped moths in wheat (Poaceae) and canola (Brassicaceae) fields in central Alberta, Canada. We captured and identified to species approximately 7900 noctuid moths. Community composition was similar in both crops. Sex pheromone–baited traps had variable specificity and low nontarget diversity. Traps baited with AAMB captured greater moth diversity than unbaited traps did. Noctuinae were the most diverse and abundant in AAMB-baited traps (62 species across 8 tribes). The AAMB lures captured more cutworm and armyworm pests than unbaited traps did. Fermented byproduct food–bait lures captured more noctuid pests than floral volatiles did. The AAMB lures can be implemented to monitor Noctuinae diversity and potentially assess local noctuid pest density in agroecosystems on the Canadian prairies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".