MétaCan
Menu
Back to cohort
Record W2083679099 · doi:10.1142/s0218348x00000056

EVIDENCE FOR A FRACTAL STOCHASTIC PROCESS UNDERLYING MEASLES EPIDEMICS IN BRITAIN

2000· article· en· W2083679099 on OpenAlexaff
Wayne S. Kendal

Bibliographic record

VenueFractals · 2000
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsOttawa Regional Cancer Foundation
Fundersnot available
KeywordsFractalMeaslesMathematicsFano factorSeries (stratigraphy)Statistical physicsEconometricsStatisticsMathematical analysisVaccinationComputer scienceMedicinePhysicsGeologyVirology

Abstract

fetched live from OpenAlex

The variability in measles incidence during the pre-vaccination period of 1944 to 1966, as recorded from 366 communities in England and Wales, was examined for properties of fractal stochastic processes. The power spectral density, Fano factor, and Allan factor were computed from the incidence time-series, and all revealed power-law scaling. As well, the distribution histogram for the weekly incidence approximated a geometric distribution. These features constituted evidence for a fractal stochastic process with underlying geometric statistics at play in the development and resolution of measles epidemics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.581
GPT teacher head0.522
Teacher spread0.059 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueFractalsSame topicCOVID-19 epidemiological studiesFrench-language works237,207