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
Robust principal components are particularly challenging to find for high‐dimensional data sets, including genomic data. Conventional principal component analysis is often unduly influenced by a few closely related family members. This phenomenon is explained using the ideas of a high‐dimensional low sample size geometric representation. These ideas further show why the earlier robust method of spherical principal components fails to solve this problem. A solution is provided, which is called the visual L1 principal component analysis (VL1PCA). This approach is based on a backwards L1‐norm best‐fit idea. VL1PCA improves upon the best previous version of L1PCA by providing interpretable scores and a scatterplot visualization of the data. Another contribution is a new notion of robust centre, the backwards L1 median. The utility of VL1PCA is illustrated on examples and a real high‐dimensional data set. Our VL1PCA is not only robust to outliers but also gives a meaningful population stratification for data even in the presence of special family structure, when other methods fail. © 2016 The Authors. Stat Published by John Wiley & Sons Ltd
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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