Using Cold As An Ally: Balsam Fir (Abies Balsamea L., Mill) Needles And Essential Oil Kill Overwintering Ticks (Ixodes Scapularis)
Bibliographic record
Abstract
Abstract The blacklegged tick, Ixodes scapularis Say, vectors Borrelia burgdorferi, a bacterium that causes Lyme Disease. Although synthetic pesticides can reduce tick numbers, less toxic acaricides would reduce impacts on other animals, such as pollinating insects. Natural-based pest control agents could provide an alternative because they have low environmental persistency; however, natural products struggle to provide effective control. We found a new natural acaricide, balsam fir (Abies balsamea) needles, that kill overwintering I. scapularis ticks. We extracted the essential oils from the needles, analyzed their chemical composition, and tested them for acaricidal activity. Both the oil and one of its main components, ß-pinene, kill overwintering ticks. Whole balsam fir needles require several weeks to kill overwintering ticks, while the essential oil is lethal within days at low temperatures (≤4°C). Further, low temperatures increase the efficacy of this low toxicity compound. Higher temperatures (i.e. 20oC) reduce the acaricidal effectiveness of the essential oil by 50% at 0.1% v/v. Low temperatures may promote the effectiveness of other low toxicity, natural control products. Winter is an overlooked season for tick control and should be explored as a possible time for the application of low toxicity products for successful tick management.
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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".