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Record W2187659169

Nearly-acyclically pushable tournaments

2001· article· es· W2187659169 on OpenAlexaff
Jing Huang, Kathryn L. B. Wood

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldComputer Science
TopicAdvanced Graph Theory Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDigraphCombinatoricsDirected acyclic graphTournamentMathematicsVertex (graph theory)Feedback arc setMultipartiteDirected graphDiscrete mathematicsGraphPhysics
DOInot available

Abstract

fetched live from OpenAlex

Let D be a digraph and X ~ V(D). By pushing X we mean reversing the orientation of each arc of D with exactly one end in X. Klostermeyer proved that it is NP-complete to decide if a given digraph can be made acyclic using the push operation. By contrast, Huang, MacGillivray, and Wood showed that the problem of deciding if a given multipartite tournament can be made acyclic using the push operation is solvable in polynomial time. We define a digraph to be nearly-acyclic if it is obtained from an acyclic digraph by substituting a (directed) triangle or a single vertex for each vertex of the acyclic digraph. It is shown that it is NPcomplete to decide if a given digraph can be made nearly-acyclic using the push operation. In this paper, we characterize, in terms of forbidden subtournaments, the tournaments which can be made nearly-acyclic by pushing. This implies that the problem of deciding if a given tournament can be made nearly-acyclic using the push operation is solvable in polynomial time. 1

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)0.000
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.324
Teacher spread0.296 · 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
Published2001
Admission routes1
Has abstractyes

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Same topicAdvanced Graph Theory ResearchFrench-language works237,207