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Record W4252931563 · doi:10.32920/ryerson.14647932.v1

The CC-Game: A Variant Of The Game Of Cops And Robbers

2021· preprint· en· W4252931563 on OpenAlexaff
Ali Haidar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsVertex (graph theory)CombinatoricsSet (abstract data type)Computer scienceVariation (astronomy)MathematicsGraphTheoretical computer scienceMathematical economicsDiscrete mathematicsPhysics

Abstract

fetched live from OpenAlex

Cops and Robbers is a vertex pursuit game played on graphs. The objective of the game, as the name suggests, is for a set of cops to catch the robber. We study a new variant of this game in which the robber can attack a cop or fight back. This variation restricts the movement of the cops and changes many of the parameters and strategies achieved in the regular game. We explore aspects of this variant such as classifications for certain cop numbers, upper and lower bounds, strategies on special graphs, the cop number on products of graphs, complexity of computations, and density of cops in infinite graphs.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.220
Teacher spread0.209 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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Same topicComputability, Logic, AI AlgorithmsFrench-language works237,207