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Record W4247213193 · doi:10.1002/spe.763

Incremental frequency count—a post BWT‐stage for the Burrows–Wheeler compression algorithm

2006· article· en· W4247213193 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSoftware Practice and Experience · 2006
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsnot available
Fundersnot available
KeywordsStage (stratigraphy)AlgorithmContext (archaeology)Entropy (arrow of time)Data compressionEntropy encodingCompression (physics)Computer scienceCoding (social sciences)MathematicsStatisticsPhysicsBiology

Abstract

fetched live from OpenAlex

Abstract The stage after the Burrows–Wheeler transform (BWT) has a key function inside the Burrows–Wheeler compression algorithm as it transforms the BWT output from a local context into a global context. This paper presents the Incremental Frequency Count stage, a post‐BWT stage. The new stage is paired with a run length encoding stage between the BWT and the entropy coding stage of the algorithm. It offers high throughput similar to a Move To Front stage, and at the same time good compression rates like the strong but slow Weighted Frequency Count stage. The properties of the Incremental Frequency Count stage are compared to the Move To Front and Weighted Frequency Count stages by their compression rates and speeds on the Calgary and large Canterbury corpora. Copyright © 2006 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.917
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.281
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it