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Record W1949868634 · doi:10.1109/lics.2003.1210074

Labeled Markov processes: stronger and faster approximations

2003· preprint· en· W1949868634 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

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMeasure (data warehouse)Markov chainMarkov processComputer scienceQuotientSimple (philosophy)State (computer science)Temporal logicModel checkingTheoretical computer scienceAlgorithmPoint (geometry)Applied mathematicsMathematicsDiscrete mathematicsPure mathematicsData miningMachine learning

Abstract

fetched live from OpenAlex

This paper proposes a measure-theoretic reconstruction of the approximation schemes developed for labeled Markov processes: approximants are seen as quotients with respect to sets of temporal properties expressed in a simple logic. This gives the possibility of customizing approximants with respect to properties of interest and is thus an important step towards using automated techniques intended for finite state systems, e.g. model checking, for continuous state systems. The measure-theoretic apparatus meshes well with an enriched logic, extended with a greatest fix-point, and gives means to define approximants which retain cyclic properties of their target.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.761
Threshold uncertainty score0.754

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 science0.0010.001
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 head0.032
GPT teacher head0.289
Teacher spread0.257 · 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

Quick stats

Citations17
Published2003
Admission routes1
Has abstractyes

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