shinyRGT: An R-Shiny application for extraction and visualization of Rat Gambling Task data
Bibliographic record
Abstract
The Rat Gambling Task (RGT) is a well validated rodent model of addiction-like behaviour. It is based on the Iowa Gambling Task (IGT) - a commonly used clinical assay to measure gambling-like behaviour. Rats choose between 4 options to earn as many sugar pellets as possible within 30 min. Each option is associated with dif- ferent reward sizes, but also the probability and duration of the time out punishment. The task is designed such that the optimal strategy for earning sugar pellets is to favour the low risk, low reward options. Consistently selecting the high risk, high reward options results in longer and more frequent time-out penalties. Currently, there is no specialized graphical user interface (GUI) designed to extract, clean, and process RGT data. The installation and use of existing tools are challenging for users lacking coding experience and can be extremely time consuming. To address these issues, we developed a free and open source R-Shiny application called shinyRGT, as a GUI for RGT data extraction, wrangling, and visualization. Clean and usable data can be easily extracted. As well, publication ready plots can be readily generated and annotated from user input. All generated tables can be downloaded as CSV files and generated graphs can be saved to local machines. shinyRGT is deployed at https://andrewcli.shinyapps.io/shinyRGT/for online use. The repository is available at https://github.com/andr3wli/ shinyapps.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.070 |
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".