A citizen science approach to investigate the distribution, abundance, and pathogen infection of vector ticks through active surveillance
Bibliographic record
Abstract
Tick-borne disease poses a growing public health burden in the United States and understanding the patterns of presence and density of infected vector ticks is key to developing and implementing effective public health management strategies. Citizen science has emerged as a highly effective means to generate data sets on the geographical distribution of tick species. But to date, nearly all citizen science studies of ticks are 'passive surveillance' programs in which researchers accept reports of ticks, together with either physical specimens or digital images, found opportunistically on people, pets, and livestock from community members for species identification and in some cases also tick-borne pathogen detection. These studies are limited because data are not collected systematically, making comparisons among locations and over time challenging, and introducing considerable reporting bias. In this study, we engaged citizen scientists in 'active surveillance' of host-seeking ticks, training volunteers to actively collect ticks on their woodland properties in an emergent region of tick-borne disease in the state of Maine, USA. We developed volunteer recruitment strategies, materials to train volunteers in data collection methods, field data collection protocols based on techniques used by professional scientists, and a variety of incentives to promote volunteer retention and satisfaction with their experiences, and we communicated research findings to participants. A total of 125 volunteers in 2020 and 181 volunteers in 2021 collected 7,246 ticks in southern and coastal Maine, including the American dog tick (Dermacentor variabilis, 4,023 specimens), the blacklegged tick (Ixodes scapularis, 3,092 specimens), and the rabbit tick (Haemaphysalis leporispalustris, 102 specimens). We demonstrated the feasibility of citizen scientists collecting ticks using active surveillance methods and found that volunteers were motivated to participate largely by their interest in the scientific problem and a desire to learn about ticks on their properties.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".