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Record W2172043318 · doi:10.1080/03610920701713286

Gamma Mixture: Bimodality, Inflexions and L-Moments

2008· article· en· W2172043318 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

VenueCommunication in Statistics- Theory and Methods · 2008
Typearticle
Languageen
FieldComputer Science
TopicBayesian Methods and Mixture Models
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBimodalityEstimatorMethod of moments (probability theory)Generalized method of momentsStatistical physicsSimple (philosophy)Parameter spaceMixture modelMathematicsApplied mathematicsDensity estimationGamma distributionBiological systemStatisticsPhysicsBiology

Abstract

fetched live from OpenAlex

We study some characteristics of the mixture of two gamma distributions. Specifically, we characterize regions of the parameter space where the mixture density is bimodal and/or has four inflexion points and further, we provide formulae for the L-moments of such mixtures. These characteristics may be useful in density and parameter estimation methods, as well as in analytical and graphical modality testing procedures. With a simple example, we illustrate the estimation of the parameters of the gamma mixture by the method of L-moments. Such estimators are in some circumstances more efficient than those based on the conventional method of moments.

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)0.005
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: Methods · Consensus signal: Methods
Teacher disagreement score0.245
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 science0.0010.001
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 head0.040
GPT teacher head0.391
Teacher spread0.351 · 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