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Record W2160794625 · doi:10.1080/10652460902867791

Fractional integrals in the matrix-variate cases and connection to statistical distributions

2009· article· en· W2160794625 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

VenueIntegral Transforms and Special Functions · 2009
Typearticle
Languageen
FieldMathematics
TopicFractional Differential Equations Solutions
Canadian institutionsMcGill University
FundersDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsMathematicsRandom variateScalar (mathematics)Connection (principal bundle)Random matrixProbability theoryMultiple integralMatrix (chemical analysis)Pure mathematicsRiemann integralFractional calculusRandom variableMathematical analysisApplied mathematicsOperator theoryFourier integral operatorStatistics

Abstract

fetched live from OpenAlex

This study examines the possible extensions of the classical fractional integral operators of scalar functions of scalar variables to the matrix-variate cases and establishes their connections to statistical distribution theory. Real-valued scalar functions of matrix argument, where the argument matrix is real and positive definite, are used in the extensions. Riemann–Liouville left-sided and right-sided fractional integral operators, Saigo integral operators and Liouville integral operators are given matrix-variate extensions. A connection is established between Riemann–Liouville fractional integrals to the distributions of sum and difference of two real positive independently distributed random variables. Some fractional integrals are also given interpretations as incomplete integrals and fractions of the total probability in statistical distribution theory.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.570
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.049
GPT teacher head0.347
Teacher spread0.298 · 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