Shiny app to visualize the results of Migratory strategy drives bird sensitivity to spring green-up (Youngflesh et al 2021)
Bibliographic record
Abstract
This repository contains code and data to create the interactive Shiny app associated with the following publication: Casey Youngflesh, Jacob Socolar, Bruna R. Amaral, Ali Arab, Robert P. Guralnick, Allen H. Hurlbert, Raphael LaFrance, Stephen J. Mayor, David A. W. Miller, and Morgan W. Tingley. 2021. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. This online app can be found at the following link at the time of the paper's publication: https://migratory-sensitivity.shinyapps.io/MigSen-app The latest version of the code to produce this app can be found on Github: https://github.com/br-amaral/MigratorySensitivity_ShinyApp Background ---------- Shiny is an R package used to create interactive applications (more info here: https://shiny.rstudio.com/). While this application is hosted on the web at the above URL (at the time of the paper's publication), it can also be reproduced locally. To produce the app, first open the app.R script in an RStudio environment with the shiny package installed. Ensure that all required packages are loaded before running the app (all required packages are loaded at the top of the script; specific package version used can be found in the README file). Click the Run App button to run the app. Note that the directory structure must be unchanged to run properly.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.411 | 0.200 |
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".