MétaCan
Menu
Back to cohort
Record W2356067764

Performance evaluation of Box Car process data compression algorithm

2003· article· en· W2356067764 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJisuanji yu yingyong huaxue · 2003
Typearticle
Languageen
FieldEngineering
TopicIndustrial Technology and Control Systems
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsCompression ratioAlgorithmCompression (physics)Data compressionProcess (computing)Computer scienceData compression ratioComputationLimit (mathematics)Interval (graph theory)Image compressionMathematicsArtificial intelligenceMaterials scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Box Car algorithm is a widely used process data compression algorithm in fieldbus control system. Its performance is affected by the recording limit and compression interval. Based on the computing result of typical simulation process data, the influence of varying the recording limit and compression interval to the compression ratio, computation time and approximation coefficient are analyzed when process trend is stable. When the process trend features drifting or periodically fluctuation, the influence of characteristics of process trend to compression ratio and approximation coefficient is studied. Results present in this paper are instructive in tuning the parameters of Box Car process data compression algorithm to obtain an ideal compression performance.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.061
GPT teacher head0.299
Teacher spread0.237 · 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