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Record W4246352112 · doi:10.1002/net.20148

Uniformly optimal digraphs for strongly connected reliability

2006· article· en· W4246352112 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNetworks · 2006
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDigraphCounterexampleConjectureCombinatoricsVertex (graph theory)Strongly connected componentReliability (semiconductor)MathematicsEnhanced Data Rates for GSM EvolutionGraphSimple (philosophy)ConnectivityTerminal (telecommunication)Vertex connectivityDirected graphDiscrete mathematicsComputer science

Abstract

fetched live from OpenAlex

Abstract Boesch et al. conjectured that for any n and m there exists a uniformly optimal ( n , m )–graph G n , m for all terminal reliability, that is, the all‐terminal reliability of G n , m is at least as large as the all‐terminal reliability of any other graph G with n vertices and m edges, no matter what the probability of an edge being operational is. Although there are counterexamples known when one restricts attention to simple graphs, the conjecture remains open when one allows parallel edges. We consider the analogous problem for strongly connected reliability, that is, the probability that a digraph contains a spanning strongly connected subdigraph, given that each vertex is operational, but arcs are independently operational with probability p . We show that there do indeed exist uniformly optimal digraphs for strongly connected ( n , m )–digraphs. We also show that if one restricts attention to simple digraphs (without parallel arcs) then such uniformly optimal digraphs need not exist. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 49(2), 145–151 2007

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.003
GPT teacher head0.176
Teacher spread0.172 · 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