Effects of trail pheromone purity, dose, and type of placement on recruiting European fire ants, Myrmica rubra, to food baits
Bibliographic record
Abstract
Trail pheromones of ants guide nest mates to a food source. Applications of synthetic trail pheromone could guide ants to poisoned food baits, which may expedite the demise of nests and help control invasive ant species. The trail pheromone of the invasive European fire ant (EFA), Myrmica rubra Linnaeus (Hymenoptera: Formicidae), has previously been identified as 3-ethyl-2,5- dimethylpyrazine. To facilitate its development as an operational EFA control tactic, our objectives were to determine the effects of (1) pheromone purity (isometrically pure or isomeric mixture), (2) pheromone dose [2, 20, 200, 2,000 ant equivalents (AEs)], and (3) type of pheromone placement (pheromone encircling a food source rather than leading towards it) on ant recruitment to baits. In laboratory binary choice experiments, isomerically pure and impure trail pheromone prompted similar recruitment responses of ants. The presence of pheromone, irrespective of dose, enhanced the recruitment of ants to food baits, with the dose of 200 AEs eliciting the strongest recruitment responses (2 AEs: 61% of foraging ants; 20 AEs: 57%; 200 AEs: 69%; 2000 AEs: 59%). Pheromone applied in a line leading towards the food bait, but not in a circle surrounding a food bait, was effective in recruiting ants, suggesting that 3-ethyl-2,5-dimethylpyrazine has a guiding but not an attractive function to EFAs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".