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Record W2296500652 · doi:10.1109/cimca.2006.65

Chaos Numbers

2006· article· en· W2296500652 on OpenAlexafffund
Chefi Ketata, Mysore G. Satish, Md Mofijul Islam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Mathematical Theories and Applications
Canadian institutionsDalhousie University
FundersAtlantic Canada Opportunities Agency
KeywordsMultiplication (music)SubtractionDivision (mathematics)ArithmeticCHAOS (operating system)ChaoticMathematicsReal numberProcess (computing)Natural numberComputer scienceDiscrete mathematicsArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

Nature is characterized by its chaotic behavior. To understand better its phenomena, it is essential to use appropriate tools to achieve the best definition possible of its chaos variables and processes. One of the main tools is mathematics. Mathematics deals with numbers. Classical mathematics falls short since it does not take into account the dynamic and evolutionary state of numbers. This paper introduces a novel meaning for numbers. Then, various arithmetic operations are established. Such operations comprise addition, subtraction, multiplication, and division. Addition is the process of calculating the sum of two or more numbers. Subtraction is the act of deducting one number from another. Multiplication is the procedure of adding a number to itself a particular number of times. Division is the operation of calculating the number of times one number is contained in another. This paper lays the foundation for chaos arithmetic, which deals with nature perpetual evolution.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.003
GPT teacher head0.227
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations5
Published2006
Admission routes2
Has abstractyes

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