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Record W4408881420 · doi:10.1134/s1995080224607550

Exploring the Fréchet–Power Rayleigh Distribution with Statistical Properties and Medical Data Insights

2024· article· en· W4408881420 on OpenAlex
Aijaz Ahmad, Aafaq A. Rather, Raymond Benjamin Afful, Andrei Volodin

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

VenueLobachevskii Journal of Mathematics · 2024
Typearticle
Languageen
FieldMathematics
TopicStatistical Distribution Estimation and Applications
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMathematicsRayleigh distributionDistribution (mathematics)Power (physics)StatisticsCalculus (dental)Algebra over a fieldStatistical physicsMathematical analysisPure mathematicsProbability density functionThermodynamicsMedicine

Abstract

fetched live from OpenAlex

Abstract In this article, we introduce a new form of Power Rayleigh distribution using the Topp–Leone generated family of distributions. The new distribution is called the Fréchet–Power Rayleigh distribution. The different structural properties of the distribution are discussed, including moments, moment-generating function, incomplete moments, order statistics, Rényi entropy, and mean deviations. The estimation of the parameters is performed by using the classical maximum likelihood method. A simulation analysis is carried out to evaluate and compare the effectiveness of estimators in terms of their bias, variance and mean square error. Finally, the distribution performance and application are investigated using real-life data from medical science.

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)0.003
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 score0.894
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.283
GPT teacher head0.356
Teacher spread0.073 · 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