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Record W3105336926

Billey-Postnikov decompositions and the fibre bundle structure of Schubert\n varieties

2014· article· en· W3105336926 on OpenAlexaff
Edward Richmond, William Slofstra

Bibliographic record

VenueeScholarship (California Digital Library) · 2014
Typearticle
Languageen
FieldMathematics
TopicAdvanced Combinatorial Mathematics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSchubert varietyMathematicsGrassmannianSchubert calculusIterated functionPure mathematicsSchubert polynomialVariety (cybernetics)Type (biology)Fiber bundleFlag (linear algebra)BundleAlgebra over a fieldMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

A theorem of Ryan and Wolper states that a type A Schubert variety is smooth if and\n only if it is an iterated fibre bundle of Grassmannians. We extend this theorem to\n arbitrary finite type, showing that a Schubert variety in a generalized flag variety is\n rationally smooth if and only if it is an iterated fibre bundle of rationally smooth\n Grassmannian Schubert varieties. The proof depends on deep combinatorial results of\n Billey-Postnikov on Weyl groups. We determine all smooth and rationally smooth Grassmannian\n Schubert varieties, and give a new proof of Peterson's theorem that all simply-laced\n rationally smooth Schubert varieties are smooth. Taken together, our results give a fairly\n complete geometric description of smooth and rationally smooth Schubert varieties using\n primarily combinatorial methods. We also give some partial results for Schubert varieties\n in Kac-Moody flag varieties. In particular, we show that rationally smooth Schubert\n varieties of affine type A are also iterated fibre bundles of rationally smooth\n Grassmannian Schubert varieties. As a consequence, we finish a conjecture of Billey-Crites\n that a Schubert variety in affine type A is smooth if and only if the corresponding affine\n permutation avoids the patterns 4231 and 3412.

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)0.002
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 score0.022
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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