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Record W2884398997 · doi:10.14745/ccdr.v41i03a04

Citizen science: Exploring its application as a tool for prodromic surveillance of vector-borne disease

2015· article· en· W2884398997 on OpenAlexafffundvenueabout
David R. Hines, SL Sibbald

Bibliographic record

VenueCanada Communicable Disease Report · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsWestern University
FundersSchulich School of Medicine and DentistryPublic Health AgencyPublic Health Agency of Canada
KeywordsCitizen scienceMobile appsDisease surveillanceInfectious disease (medical specialty)Internet privacyPublic healthPublic relationsDownloadPublic health surveillanceCrowdsourcingData scienceDiseaseComputer sciencePolitical scienceMedicineWorld Wide WebBiology

Abstract

fetched live from OpenAlex

Citizen science is the systematic collection and analysis of data, development of technology, testing of natural phenomena and the dissemination of these activities by researchers on a primarily avocational or voluntary basis. The application of citizen science-informed mobile applications (apps) provides a means for Canadians to participate in the surveillance of infectious disease. This article makes the case for a mobile application that can be used to enhance the surveillance of vector-borne diseases in Canada. Lyme disease is used as an example due to its increasing incidence and lack of available real-time information. The authors also suggest how such an app could be designed and used in a way that would attract end users to download and use it as a public health tool. If successful, these type of apps could serve as supplements to active surveillance programs as well as a means for bidirectional communication between public health professionals and citizens.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.040
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.277
Teacher spread0.218 · 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

Citations12
Published2015
Admission routes4
Has abstractyes

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