Rdataretriever: R Interface to the Data Retriever
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rdataretriever provides an R interface to the Python-based Data Retriever software. The Data Retriever automates the multiple steps of data analysis including downloading, cleaning, standardizing, and importing datasets into a variety of relational databases and flat file formats. It also supports provenance tracking for these steps of the analysis workflow by allowing datasets to be committed at the time of installation and allowing them to be reinstalled with the same data and processing steps in the future. Finally, it supports the installation of spatial datasets into relational databases with spatial support. The rdataretriever provides an R interface to this functionality and also supports importing of datasets directly into R for immediate analysis. The system also supports the use of custom data processing routines to support complex datasets that require custom data manipulation steps. The Data Retriever and rdataretriever are focused on scientific data applications including a number of widely used, but difficult to work with, datasets in ecology and the environmental sciences.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.007 | 0.027 |
| Open science | 0.038 | 0.036 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it