The PAQ1 Data Compression Program
Bibliographic record
Abstract
This paper describes the PAQ1 lossless data compression program. PAQ1 is an arithmetic encoder using a weighted average of five bit-level predictors. The five models are: (1) a bland model with 0 or 1 equally likely, (2) a set of order-1 through 8 nonstationary n-gram models, (3) a string matching model for n-grams longer than 8, (4) a nonstationary word unigram and bigram model for English text, and (5) a positional context model for data with fixed length records. Probabilities are weighted roughly by n /tp(0)p(1) where n is the context length, t is the age of the training data (number of subsequent events), and p(0) and p(1) are the probabilities of a 0 or 1 (favoring long runs of zeros or ones). The aging of training statistics makes the model nonstationary, which gives excellent compression for mixed data types. PAQ1 compresses the concatenated Calgary corpus to 1.824 bits per character, which is 4.5% better than RK (Taylor, 1999) and 2.9% better than PPMONSTR (Shkarin, 2001), the top programs rated by Gilchrist (2001) and Ratushnyak (2001) respectively, although those programs do slightly better on homogeneous data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".