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Mobile Fractal Generation

2006· book-chapter· en· W2500852528 on OpenAlexaff
Daniel C. Doolan, Sabin Tabirca, Laurence T. Yang

Bibliographic record

VenueIGI Global eBooks · 2006
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsFractalMandelbrot setMobile phoneComputer scienceSet (abstract data type)Image (mathematics)Resource (disambiguation)Artificial intelligenceMultimediaTelecommunicationsComputer networkMathematics

Abstract

fetched live from OpenAlex

Ever since the discovery of the Mandelbrot set, the use of computers to visualise fractal images have been an essential component. We are looking at the dawn of a new age, the age of ubiquitous computing. With many countries having near 100% mobile phone usage, there is clearly a potentially huge computation resource becoming available. In the past years there have been a few applications developed to generate fractal images on mobile phones. This chapter discusses three possible methodologies whereby such images can be visualised on mobile devices. These methods include: the generation of an image on a phone, the use of a server to generate the image and finally the use of a network of phones to distribute the processing task.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.002

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 head0.025
GPT teacher head0.209
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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