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Record W2407759797

An alternative a pproach to the classification of the regular near hexagons with parameters (s, t, t 2 ) = (2, 11, 1).

2015· article· en· W2407759797 on OpenAlexvenueno aff
Bart De Bruyn

Bibliographic record

VenueArs Combinatoria · 2015
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsnot available
Fundersnot available
KeywordsUniquenessAddendumBinary Golay codeTernary Golay codeMathematicsTernary operationCharacterization (materials science)Code (set theory)Section (typography)CombinatoricsElementary proofDiscrete mathematicsPure mathematicsAlgorithmComputer scienceMathematical analysisProgramming languageLinear codeLaw
DOInot available

Abstract

fetched live from OpenAlex

Based on some results of Shult and Yanushka [7], Brouwer [1] proved that there exists a unique regular near hexagon with parameters (s,t, t(2)) = (2, 11, 1), namely the one related to the extended ternary Golay code. His proof relies on the uniqueness of the Witt design S(5, 6,12), Pless's characterization of the extended ternary Golay code G(12) and some properties of S(5, 6,12) and G(12). It is possible to avoid all this machinery and to give an alternative more elementary and self-contained proof for the uniqueness. It was only observed recently by the author that such an alternative proof is implicit in the literature: it can be obtained by combining some results from the papers [1], [4] and [7]. This survey paper has the aim to bring this fact to the attention of the mathematical community. We describe the parts of the above papers which are relevant for this alternative proof of the classification. The alternative proof also requires that we prove a number of extra facts which are not explicitly contained in any of the three above papers. The present paper can also been seen as an addendum to Section 6.5 of the book [3] where the uniqueness of the near hexagon was not proved.

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)0.001
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.030
GPT teacher head0.247
Teacher spread0.217 · 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 teacher head, 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

Citations0
Published2015
Admission routes1
Has abstractyes

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