Highlights of Medical Entomology 2018: The Importance of Sustainable Surveillance of Vectors and Vector-Borne Pathogens
Bibliographic record
Abstract
The theme of the 2018 Entomological Society of America, Entomological Society of Canada, and Entomological Society of British Columbia Joint Annual Meeting was Crossing Borders: Entomology in a Changing World. Following the theme of the meeting, papers selected for the 2018 'Highlights of Medical Entomology' included reports of expanded ranges for Aedes albopictus (Skuse) (Diptera: Culicidae), Aedes japonicus japonicus (Theobald) (Diptera: Culicidae), Culex coronator Dyar and Knab (Diptera: Culicidae), Mansonia titillans (Walker) (Diptera: Culicidae), and Wyeomyia mitchellii (Theobald) (Diptera: Culicidae); a first report of mosquito feeding on earthworms and leeches; Cache Valley virus detection in Ae. j. japonicus; surveillance in high-risk areas of Canada for Lyme Disease; and increasing lone-star tick detections in Connecticut. The selection of papers served as a reminder of the increasingly difficult challenge of sustaining vector surveillance programs. Fluctuating funding and a decline in vector taxonomists and identification expertise contribute to the erosion of vector surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".