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Connection Coefficients for Higher-order Bernoulli and Euler Polynomials: A Random Walk Approach

2019· article· en· W2891872939 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

Venue˜The œFibonacci quarterly · 2019
Typearticle
Languageen
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsConnection (principal bundle)Bernoulli's principleEuler's formulaRandom walkOrder (exchange)MathematicsBernoulli polynomialsDifference polynomialsMathematical analysisOrthogonal polynomialsPhysicsGeometryStatisticsThermodynamics

Abstract

fetched live from OpenAlex

We use random walks as an approach to obtain connection coefficients for higherorder Bernoulli and Euler polynomials. In particular, we study the cases of a 1-dimensional linear reflected Brownian motion and of a 3-dimensional Bessel process. By considering the successive hitting times of two, three, and four fixed levels of these random walks, we obtain non-trivial identities that involve higher-order Bernoulli and Euler polynomials.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.618

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 science0.0000.000
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 head0.023
GPT teacher head0.282
Teacher spread0.258 · 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