TDApplied: An R package for machine learning andinference with persistence diagrams
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
Topological data analysis is a collection of tools, based on the mathematical fields of topology and geometry, for finding structure in whole datasets.Its main tool, persistent homology (Edelsbrunner et al., 2000;Zomorodian & Carlsson, 2005), computes a shape descriptor of a dataset called a persistence diagram which encodes information about holes that exist in the dataset (example applications span a variety of areas, see for example Gracia-Tabuenca et al. ( 2020), Haim Meirom & Bobrowski (2022), and Krishnapriyan ( 2021)).These types of features cannot be identified by other methods, making persistence diagrams a unique and valuable data science object for studying and comparing datasets.The two most popular data science tools for analyzing multiple objects are machine learning and inference, but to date there has been no open source implementation of published methods for machine learning and inference of persistence diagrams.
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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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