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Record W2784087054 · doi:10.1002/rsa.20841

Enumeration and randomized constructions of hypertrees

2019· preprint· en· W2784087054 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRandom Structures and Algorithms · 2019
Typepreprint
Languageen
FieldComputer Science
TopicTopological and Geometric Data Analysis
Canadian institutionsnot available
FundersH2020 European Research CouncilAzrieli Foundation
KeywordsEnumerationVertex (graph theory)Homology (biology)CombinatoricsMathematicsFace (sociological concept)Upper and lower boundsDiscrete mathematicsPhysicsBiologyGeneticsGraphMathematical analysis

Abstract

fetched live from OpenAlex

Over 30 years ago, Kalai proved a beautiful d ‐dimensional analog of Cayley's formula for the number of n ‐vertex trees. He enumerated d ‐dimensional hypertrees weighted by the squared size of their ( d − 1)‐dimensional homology group. This, however, does not answer the more basic problem of unweighted enumeration of d ‐hypertrees, which is our concern here. Our main result, Theorem 1.4, significantly improves the lower bound for the number of d ‐hypertrees. In addition, we study a random 1‐out model of d ‐complexes where every ( d − 1)‐dimensional face selects a random d ‐face containing it, and show that it has a negligible d ‐dimensional homology.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.665

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 science0.0000.001
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 head0.240
Teacher spread0.229 · 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