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Record W4220778679 · doi:10.5539/jmr.v14n2p39

Modified Extended Inverted Weibull Distribution with Application to Neck Cancer Data

2022· article· en· W4220778679 on OpenAlexvenueno aff
Ogunde Adebisi Ade, Oseghale Osezuwa Innocent, Fatoki Olayode, Laoye Victoria Eshomomoh

Bibliographic record

VenueJournal of Mathematics Research · 2022
Typearticle
Languageen
FieldMathematics
TopicStatistical Distribution Estimation and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsWeibull distributionMathematicsExponentiated Weibull distributionStatisticsMoment-generating functionResidualApplied mathematicsGenerating functionEntropy (arrow of time)Statistical physicsProbability density functionMathematical analysisAlgorithmThermodynamics

Abstract

fetched live from OpenAlex

This work introduces a new three-parameter modified extended inverted Weibull (MEIW) distribution which is a hybrid of the one-parameter inverted Weibull distribution. The density function of the MEIW can be expressed as a linear combination of the inverted Weibull densities. Some mathematical properties of the proposed MEIW model such as ordinary and incomplete moments, mean residual life, and mean waiting time, Tsallis entropy, moment generating function and order statistics are investigated. The maximum likelihood estimation method is considered to estimate the parameters of the MEIW model. The relevance of the MEIW model is studied via an application to neck cancer data.

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)0.003
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.282
GPT teacher head0.493
Teacher spread0.210 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2022
Admission routes1
Has abstractyes

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