USE OF GEOGRAPHIC INFORMATION SYSTEMS TO ASSESS THE FEASIBILITY OF GROUND- AND AERIAL-BASED ADULTICIDING FOR WEST NILE VIRUS CONTROL IN BRITISH COLUMBIA, CANADA
Bibliographic record
Abstract
Geographic Information Systems (GIS) analysis of 34 forecasted high West Nile virus (WNV) risk communities in British Columbia (BC), Canada was useful to assess feasibility and planning of the operational logistics of an emergency spray event in advance of a WNV outbreak. The geographic coverage and operational time required to perform ground- and aerial-based ultra-low volume (ULV) adulticiding were calculated using GIS. The mean geographic coverages of the ground-, aerial-, and combination of ground- and aerial-based adulticiding strategies were 39%, 61%, and 69%, respectively. The driving distance, driving time, and number of treatment nights required to perform ground-based spraying of an entire community were also calculated. Due to the large variability of treatment coverage estimates within and among the communities, no single treatment method was identified as the best strategy for province-wide ULV adulticiding in BC. Instead, the strategy for each community should be examined individually with local knowledge and expertise.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".