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Record W2605400039 · doi:10.5539/ijsp.v6n3p183

Characterizations Based on Cumulative Entropy of the Last-order Statistics

2017· article· en· W2605400039 on OpenAlexvenueno aff
Osman Kamari

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2017
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsCumulative distribution functionMaximum entropy probability distributionWeibull distributionStatisticsEntropy (arrow of time)Maximum entropy thermodynamicsOrder statisticResidual entropyPrinciple of maximum entropyMin entropyEntropy rateStatistical physicsBinary entropy functionProbability density functionThermodynamicsConfiguration entropyPhysics

Abstract

fetched live from OpenAlex

Cumulative Entropy (CE) as a measure of uncertainty alternative to Shannon entropy is proposed by Di Crescenzo and Longobardi (2009). In this paper, some properties of the cumulative entropy are derived and under conditions are showed the cumulative entropy of the last order statistics can determine the distribution function uniquely. Weibull family is characterized by ratio of the cumulative entropy of the last order statistics to its expectation. Also, some inequalities are presented for the cumulative entropy of reversed residual lifetime of a parallel system.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.228

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 studies0.0000.000
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2017
Admission routes1
Has abstractyes

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