A corpus for the evaluation of lossless compression algorithms
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
A number of authors have used the Calgary corpus of texts to provide empirical results for lossless compression algorithms. This corpus was collected in 1987, although it was not published until 1990. The advances with compression algorithms have been achieving relatively small improvements in compression, measured using the Calgary corpus. There is a concern that algorithms are being fine-tuned to this corpus, and that small improvements measured in this way may not apply to other files. Furthermore, the corpus is almost ten years old, and over this period there have been changes in the kinds of files that are compressed, particularly with the development of the Internet, and the rapid growth of high-capacity secondary storage for personal computers. We explore the issues raised above, and develop a principled technique for collecting a corpus of test data for compression methods. A corpus, called the Canterbury corpus, is developed using this technique, and we report the performance of a collection of compression methods using the new corpus.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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