Synergistic attraction of Western black-legged ticks, <i>Ixodes pacificus</i> , to CO <sub>2</sub> and odorant emissions from deer-associated microbes
Bibliographic record
Abstract
Foraging ticks reportedly exploit diverse cues to locate their hosts. Here, we tested the hypothesis that host-seeking Western black-legged ticks, Ixodes pacificus , and black-legged ticks, I. scapularis , respond to microbes dwelling in sebaceous gland secretions of white-tailed deer, Odocoileus virginianus , the ticks' preferred host. Using sterile wet cotton swabs, microbes were collected from the pelage of a sedated deer near forehead, preorbital, tarsal, metatarsal and interdigital glands. Swabs were plated on agar, and isolated microbes were identified by 16S rRNA amplicon sequencing. Of 31 microbial isolates tested in still-air olfactometers, 10 microbes induced positive arrestment responses by ticks, whereas 10 others were deterrent. Of the 10 microbes prompting arrestment by ticks, four microbes—including Bacillus aryabhattai (isolates A4)—also attracted ticks in moving-air Y-tube olfactometers. All four of these microbes emitted carbon dioxide and ammonia as well as volatile blends with overlapping blend constituents. The headspace volatile extract (HVE) of B. aryabhattai (HVE-A4) synergistically enhanced the attraction of I . pacificus to CO 2 . A synthetic blend of HVE-A4 headspace volatiles in combination with CO 2 synergistically attracted more ticks than CO 2 alone. Future research should aim to develop a least complex host volatile blend that is attractive to diverse tick taxa.
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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".