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Record W2024128585 · doi:10.1109/iccnc.2013.6504198

Polar codes for data storage applications

2013· article· en· W2024128585 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

Venue2013 International Conference on Computing, Networking and Communications (ICNC) · 2013
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceDecoding methodsEncoding (memory)PolarComputer data storageError detection and correctionThroughputAlgorithmTheoretical computer scienceComputer engineeringTelecommunicationsComputer hardwareArtificial intelligence

Abstract

fetched live from OpenAlex

Polar codes are a new class of error-correcting codes that provably achieve the capacity of memoryless channels with low complexity encoding and decoding algorithms. In this work, we survey the literature and investigate the suitability of polar codes to data storage applications focusing on error-correction performance and throughput. We show that polar codes meet the criteria for such applications and highlight the work required before practical data storage systems can utilize these codes.

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 categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0070.003
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 head0.122
GPT teacher head0.352
Teacher spread0.231 · 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