General Food Semiochemicals Attract Omnivorous German Cockroaches, Blattella germanica
Bibliographic record
Abstract
Stale beer and peanut butter are effective baits for the German cockroach (GCRs), Blattella germanica (L.) (Dictyoptera: Blattellidae). In still-air arena olfactometer experiments it was previously shown that headspace volatile extracts of peanut butter and solvent extract of beer attract male GCRs. The objective of this study was to identify the semiochemicals that mediate attraction of GCRs to these sources. Coupled gas chromatographic-electroantennographic detection (GC-EAD) and GC-mass spectrometric (MS) analyses of these attractive extracts, or fractions thereof, and of synthetic standards revealed many candidate semiochemicals. Elaborate olfactometer experiments determined that 1-hexanol from peanut butter, and ethanol and 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP) from beer, are the key semiochemicals of these food sources. 1-Hexanol is a well-known headspace volatile of decomposing lipids, ethanol conveys food fermentation, and DDMP with a caramel-type flavor has been found in several types of food. By responding to these rather general food-derived compounds, the omnivorous GCRs appear to exploit semiochemicals that indicate the presence of various food components, such as lipids and carbohydrates. Synthetic equivalents of these semiochemicals may be formulated as baits or be added to, and thus enhance the attractiveness of, natural food sources as trap or insecticidal baits.
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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".