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Record W2121639913 · doi:10.2987/5609.1

USE OF GEOGRAPHIC INFORMATION SYSTEMS TO ASSESS THE FEASIBILITY OF GROUND- AND AERIAL-BASED ADULTICIDING FOR WEST NILE VIRUS CONTROL IN BRITISH COLUMBIA, CANADA

2007· article· en· W2121639913 on OpenAlexafffundabout
Sunny Mak, MIEKE BULLER, Allen Furnell, Laura MacDougall, Bonnie Henry

Bibliographic record

VenueJournal of the American Mosquito Control Association · 2007
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsBC Centre for Disease Control
FundersNational Research Council CanadaBritish Columbia Centre for Disease Control
KeywordsWest Nile virusGeographic information systemOutbreakGeographyCartographyBiologyVirusVirology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.256
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2007
Admission routes3
Has abstractyes

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