Creating web applications for spatial epidemiological analysis and mapping in R using Rwui
Bibliographic record
Abstract
BACKGROUND: Creating a user friendly web based application which executes an R script allows physicians, epidemiologists, and others unfamiliar with the statistical language to perform powerful statistical analyses easily. The geographic mapping of data is an important tool in spatial epidemiological analysis, and the R project includes many tools for such analyses, but few for visualization. Hence, web applications that run R for epidemiological analysis need to be able to present the results in a geographic format. RESULTS: Rwui is a web application for creating web based applications for running R scripts. We describe updates to Rwui that enable it to create web applications for R scripts which return the results of the analysis to the web page as geographic maps. CONCLUSIONS: Rwui enables statisticians to create web applications for R scripts without the need to learn web programming. Creating a web application provides users access to an R based analysis without the need to learn R. Recent updates to Rwui have increased its applicability in the field of spatial epidemiological analysis.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
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".