Female ticks ( <i>Ixodes scapularis</i> ) infected with <i>Borrelia burgdorferi</i> have increased overwintering survival, with implications for tick population growth
Bibliographic record
Abstract
The tick, Ixodes scapularis , vectors pathogens such as Borrelia burgdorferi , the bacterium that causes Lyme Disease. Over the last few decades I. scapularis has expanded its range, introducing a novel health threat into these areas. Warming temperatures appear to be one cause of its range expansion to the north. However, other factors are also involved. We show that unfed adult female ticks infected with B. burgdorferi have greater overwintering survival than uninfected female ticks. Locally collected adult female ticks were placed in individual microcosms and allowed to overwinter in both forest and dune grass environments. In the spring we collected the ticks and tested both dead and living ticks for B. burgdorferi DNA. Infected ticks had greater overwintering survival compared with uninfected ticks every winter for three consecutive winters in both forest and dune grass environments. We discuss the most plausible explanations for this result. The increased winter survival of adult female ticks could enhance tick population growth. Our results suggest that, in addition to climate change, B. burgdorferi infection itself may be promoting the northern range expansion of I. scapularis . Our study highlights how pathogens could work synergistically with climate change to promote host range expansion.
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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) | 0.003 | 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".