Replication Data for: Rhinovirus transmission dynamics across different social structures
Bibliographic record
Abstract
This is a replication dataset for the publication titled: "Rhinovirus transmission dynamics across different social structures ." This dataset aims to compare rhinovirus dynamics from five previous studies conducted in Kenya in four different social structures. The studies are (i) intensive household surveillance in coastal Kenya (Dec 2009 - May 2010), (ii) surveillance of respiratory viruses within a public primary school (May 2017 – April 2018), (iii) outpatient surveillance of acute respiratory illness within the Kilifi Health and Demographic Surveillance System (KHDSS) (Dec 2015 - Nov 2016), and (iv) countrywide surveillance of severe acute respiratory syndrome illness (SARI) among inpatients and influenza-like illness (ILI) among outpatients via sentinel hospital reporting (Jan 2014 - Dec 2014). (v) We used contemporaneous data from long-term surveillance of severe pneumonia among pediatric inpatients at the Kilifi County Hospital (KCH) for comparison with the households, school, and KHDSS studies datasets. The primary dataset contains VP4/2 sequence data, alongside necessary metadata (e.g. date of sampling, site where collection, respective study). Secondary datasets generated from the primary data are also included (described in section 4 - Contents).
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.131 | 0.080 |
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".