Traps baited with dry ice outperform cloth drags for capturing ticks (Acari: Ixodidae) in 3 widely separated geographic regions
Bibliographic record
Abstract
Surveillance is crucial for monitoring tick populations and assessing disease risk. We tested the hypothesis that dry ice-baited traps with a downward-facing sticky surface and traditional drag cloths would be equally effective in capturing ticks in Texas, Oklahoma, and Wisconsin. Experiments ran for 69 to 100 d in the spring-summer of 2023 with three, 60-m-long rows of 4 traps each, spaced 20 m apart, perpendicular to 3, 60-m-long dragging transects with traps spaced 10 m on either side. Traps captured 84.2% of a total of 25,596 ticks, and 64.3% after adjusting the data to equalize the number of person-hours expended for each sampling method. For all 3 life stages of lone star ticks, Amblyomma americanum (L.), traps almost always caught the most ticks per person-hour. For larvae and nymphs of blacklegged ticks, Ixodes scapularis (Say), and adult American dog ticks, Dermacentor variabilis (Say), trapping was either superior or similar to dragging. Correlation coefficients comparing numbers caught by trapping and dragging were generally positive for all 3 species. The magnitude of dragging needed to match the total catch in 1 trap ranged from 323 m2 for D. variabilis in Wisconsin to 511 m2 for A. americanum in Oklahoma. Trapping was also more sensitive than dragging at detecting rare tick species and experienced fewer failures to detect the presence of any ticks. These findings suggest that trapping holds promise as an alternative or supplement to current surveillance methods, pending development of a cost-effective commercial trap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".