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Record W2000898864 · doi:10.1007/s11134-014-9431-9

Perfect sampling of a single-server queue with periodic Poisson arrivals

2014· article· en· W2000898864 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueQueueing Systems · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAdvanced Queuing Theory Analysis
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQueuePoisson distributionCombinatoricsMathematicsDiscrete mathematicsSampling (signal processing)Integer (computer science)Computer scienceStatisticsTelecommunicationsComputer network

Abstract

fetched live from OpenAlex

In this paper we present algorithms for the perfect sampling of single-server time-varying queues with periodic Poisson arrivals under the first come first served (FCFS) discipline. The service durations have periodically time-dependent exponential ( $$\mathrm M _t/\mathrm M _t/1$$ ) or homogeneous general ( $$\mathrm M _t/\mathrm G /1$$ ) distributions. Assuming a cycle length of 1, we construct discrete dominating processes at the integer instants $$n \in \{0, \pm 1, \ldots \}$$ . Perfect sampling of the $$\mathrm M _t/\mathrm M _t/1$$ queue is obtained using dominated CFTP (Kendall and Møller 2000) when the system is relatively lightly loaded or with the regenerative method (Sigman 2012) in the general case. For the $$\mathrm M _t/\mathrm G /1$$ queue, perfect sampling is achieved with dominated CFTP.

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 score0.453
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.224
Teacher spread0.204 · 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