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Record W2371739233 · doi:10.5539/jmr.v9n3p75

A Note on the Structure of Roller Coaster Permutations

2017· article· en· W2371739233 on OpenAlexvenueno aff
William Adamczak

Bibliographic record

VenueJournal of Mathematics Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenome Rearrangement Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsRoller coasterPermutation (music)MathematicsCombinatoricsClass (philosophy)Parity of a permutationDiscrete mathematicsCyclic permutationComputer scienceSymmetric groupArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

In this paper we consider the structure of a special class of permutations known as roller coaster permutations, first introduced by Ahmed & Snevily (2013). A roller coaster permutation is described as, a permutation that maximizes the total switches from ascending to descending, or visa versa, for the permutation as well as all of its subpermutations, simultaneously. This paper looks at the structure of these permutations, particularly the alternating structure, what the entires of these permutations can look like, we then introduce a notion of a condition stronger than alternating that we shall refer to as recursively alternating.

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)0.006
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 score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0020.005
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.002

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 head0.083
GPT teacher head0.404
Teacher spread0.321 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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