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Record W2279447487 · doi:10.3855/jidc.8248

ZIKATracker: A mobile App for reporting cases of ZIKV worldwide

2016· article· en· W2279447487 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Infection in Developing Countries · 2016
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity Health NetworkCerebral Diagnostics (Canada)
Fundersnot available
KeywordsZika virusPortugueseMedicineVirologyVirus

Abstract

fetched live from OpenAlex

We have developed a mobile App called ZIKATracker (zikatracker.net) to voluntarily be used to report ZIKV cases on a public or private level. As the Zika virus (ZIKV) infection zones are rapidly expanding across South, Central, and North America, and reports have emerged linking ZIKV infection with developmental defects and neurological sequelae, reporting the movement and sequelae of ZIKV is essential. ZIKATracker is a multi-lingual App (English, French, Spanish, and Portuguese) freely available to anyone worldwide wishing to report a suspected or confirmed case of Zika virus and related symptoms. Knowledge gained from the use of this App will help direct the implementation of mosquito control measures in needed areas, bring aid to those affected by the Zika virus, and understand the movement and sequelae of ZIKV as it spreads through communities and across continents.

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.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.152
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0000.000
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 head0.021
GPT teacher head0.320
Teacher spread0.298 · 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