Balsam fir (Abies balsamea) needles and their essential oil kill overwintering ticks (Ixodes scapularis) at cold temperatures
Bibliographic record
Abstract
The blacklegged tick, Ixodes scapularis, vectors Borrelia burgdorferi, a bacterium that causes Lyme Disease. Although synthetic pesticides can reduce tick numbers, there are concerns about their potential effects on beneficial insects, such as pollinators. Plant-based pest control agents such as essential oils could provide an alternative because they have low environmental persistency; however, these 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 oil from the needles, analyzed its chemical composition, and tested it for acaricidal activity. We placed ticks in tubes with substrate and positioned the tubes either in the field or in incubators simulating winter temperatures. We added balsam fir essential oil, or one of the main components of balsam fir essential oil (i.e., ß-pinene), to each tube. We found that both the oil and 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 increased the efficacy of this volatile essential oil. Higher temperatures (i.e., 20 °C) reduce the acaricidal effectiveness of the essential oil by 50% at 0.1% v/v. Low temperatures may promote the effectiveness of other 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".