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Record W2067030853 · doi:10.1186/2195-5832-1-1

Editorial: Journal of Statistical Distributions and Applications

2014· editorial· en· W2067030853 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Statistical Distributions and Applications · 2014
Typeeditorial
Languageen
FieldMathematics
TopicStatistical Distribution Estimation and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsStatistical inferenceStatistical modelComputer scienceStatistical theoryParametric statisticsPoisson distributionProbability distributionOperations researchStatisticsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

© A m Introduction Statistical distributions are the foundations of statistical methodology in both theory and applications. They are the back bone of every parametric statistical method, including inference, modeling, survival, reliability, and others. In recent years, partly due to the advanced computing technology, there have been a series of developments of new methodology for generating new families of statistical distributions, which have greatly enhanced parametric statistical methods for handling real world scenarios that could not be modeled using existing distributions. One main reason for the need of generalized families is that each of the useful basic statistical distributions has its own weakness in real-world applications. The real-world phenomena are often much more complex for these commonly known basic statistical distributions to provide adequate fit. For example, Johnson et al. (2005) presented various modifications and generalizations of the Poisson distribution. Some of these distributions were developed in an attempt to explain the unequal mean and variance in the numerical data observed in different fields of applications. The history of statistical distributions started in the 18 century. The first major gathering on statistical distributions and their applications was in 1963 at McGill, Canada where experts participated in the International Symposium on Classical and Contagious Discrete Distributions. Another major gathering, the NATO Advanced Study Institute on Statistical Distributions in Scientific Work, was held at the University of Calgary, Canada from July 29 to August 10, 1974. Patil et al. (1974) in their preface to the Proceedings of the NATO Advanced Study Institute, referred to the McGill symposium as “a milestone in the recognition and development of the theory and application of statistical distributions.” According to Professor Kotz in his Foreword to the book by Consul and Famoye (2006) “The main impetus for the development of an orderly investigation of statistical distributions and their applications was the International Symposium on Classical and Contagious Discrete Distributions, organized by G.P. Patil in August of 1969 (1963) ...” Since then, various conferences and meetings have been organized on statistical distributions. However, no dedicated journal on statistical distributions was ever launched. In one of the Lukacs Symposia in the early 1990s held at Bowling Green State University, Bowling Green, Ohio, Dr. Adrienne Kemp (University of St. Andrews in Scotland) in her address stated that all research articles on statistical distributions are scattered in various journals and there is no dedicated journal for statistical distributions. To

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.017
GPT teacher head0.348
Teacher spread0.331 · 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