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Record W2502872774 · doi:10.1017/cbo9781139034357.008

Practical methods for MDC design

2011· book-chapter· en· W2502872774 on OpenAlexaff
Nima Sarshar, Xiaolin Wu, Jia Wang, Sorina Dumitrescu

Bibliographic record

VenueCambridge University Press eBooks · 2011
Typebook-chapter
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsMcMaster UniversityUniversity of Regina
Fundersnot available
KeywordsNetwork packetComputer scienceMultiple description codingLossy compressionCoding (social sciences)Computer networkLinear network codingTheoretical computer scienceDistributed computingMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Multiple-description codes are powerful tools for network-aware source coding and communication, as suggested in the previous chapters. We showed how MDC can be useful even in error-free networks. MDC, however, traditionally has been used to combat losses in packet lossy networks in which packets are likely to be dropped or lost. In this chapter, we review practical techniques for construction and optimization of MDCs.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.142
GPT teacher head0.327
Teacher spread0.185 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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