ZIKATracker: A mobile App for reporting cases of ZIKV worldwide
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.026 |
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".