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Record W2022963826 · doi:10.1109/tcomm.2006.877945

Length-constrained MAP decoding of variable-length encoded Markov sequences

2006· article· en· W2022963826 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

VenueIEEE Transactions on Communications · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDecoding methodsMaximum a posteriori estimationMarkov chainDirected acyclic graphMathematicsParameterized complexityAlgorithmDirected graphMarkov processPath (computing)Sequential decodingSequence (biology)Binary numberMarkov modelMathematical optimizationComputer scienceMaximum likelihoodBlock code

Abstract

fetched live from OpenAlex

In this paper, we consider the problem of length-constrained maximum a posteriori decoding of a Markov sequence that is variable-length encoded and transmitted over a binary symmetric channel. We convert this problem into one of a maximum-weight k-link path in a weighted directed acyclic graph. The induced graph-optimization problem can be solved by a fast parameterized search algorithm that finds either the optimal solution with high probability, or a good approximate solution otherwise. The proposed algorithm has lower complexity and superior performance than the previous approximation algorithms

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.530

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.261
Teacher spread0.241 · 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