A comparison of BG Sentinel and CDC trap attractants for mosquito surveillance in urban and suburban areas of Montgomery and Prince George's Counties, Maryland, U.S.A.
Bibliographic record
Abstract
Monitoring mosquito populations is crucial for vector-borne disease surveillance. Routine mosquito surveillance in many regions of the United States is performed either by vector abatement districts or public health departments. These surveillance programs often use multiple trap types and attractants to target key mosquito species, however setting different traps with varying attractants can be expensive and labor intensive. Because funding for mosquito control is highly variable throughout the U.S., some programs may be limited in their surveillance capabilities. To determine whether a single trap-attractant combination could provide specificity for key vector and nuisance species, as well as sensitivity for rare species, we compared the BG-Sentinel 2 and CDC miniature light traps paired with CO2, UV-LED, BG Lure, BG Sweetscent, octenol, or chicken feathers. Trapping was conducted biweekly from June/July-October 2019 and 2020 in Montgomery and Prince George's County, MD. BG traps collected significantly more Aedes albopictus than CDC traps when paired with BG Lure, Sweetscent, or octenol. BG/CO2 traps collected both the greatest number of total mosquitoes and Culex pipiens. BG/CO2, CDC/CO2, and CDC/UV traps provided the most diverse collections. Trapping with the CO2-baited BG-Sentinel is recommended as an effective strategy for general mosquito surveillance when resources are limited.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".