Crowdsourcing for large-scale mosquito (Diptera: Culicidae) sampling
Bibliographic record
Abstract
Abstract Sampling a cosmopolitan mosquito (Diptera: Culicidae) species throughout its range is logistically challenging and extremely resource intensive. Mosquito control programmes and regional networks operate at the local level and often conduct sampling activities across much of North America. A method for large-scale sampling of two mosquito species using crowdsourcing to network with these local and regional programmes is described. A total of 961 mosquito vector and control districts, health departments, and individual collectors across the United States of America and Canada were contacted in 2011 and 2012 of which 9% positively responded by sending mosquitoes. In total, 1101 unique population samples of Aedes vexans (Meigen) and Culex tarsalis Coquillett were collected throughout their range in these two countries. Aedes vexans outgroup samples were also submitted from Europe and Asia. This is the largest crowd-sourced collection of samples to date.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".