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Record W4250645191 · doi:10.1109/wsc.1988.716197

Linear congruential generators of order K<1

2005· article· en· W4250645191 on OpenAlexafffund
P. L'Ecuyer, F. Blouin

Bibliographic record

Venue1988 Winter Simulation Conference Proceedings · 2005
Typearticle
Languageen
FieldMathematics
TopicAnalytic Number Theory Research
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLinear congruential generatorMathematicsPseudorandom number generatorModuloHyperplaneDiscrete mathematicsOrder (exchange)Class (philosophy)ComputationCombinatoricsFinite fieldAlgorithmComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

We present a class of random number generators defined by a linear congruential recursion of order k > 1, modulo m, and whose period can attain m/sup k/ - 1. The spectral test can be extended to this class of generators, and permits the computation of the distance between the successive t-dimensional hyperplanes in which lie all the t-tuples of successive values. In terms of the spectral test, these generators could be almost as good as the best regular (order 1) linear congruential generators with modulus close to m/sup k /- 1. We discuss the implementation of computer programs to search for maximal period generators of this kind and to apply the (generalized) spectral test. We also present the results of a search to find good vectors of multipliers, for some values of the modulus m, and give an example of a portable implementation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.097
GPT teacher head0.381
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations6
Published2005
Admission routes2
Has abstractyes

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