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Record W4415340732 · doi:10.1016/j.tcs.2025.115597

Degrees are useless in Snort when measuring temperature

2025· article· en· W4415340732 on OpenAlexafffund
Svenja Huntemann, Tomasz Maciosowski

Bibliographic record

VenueTheoretical Computer Science · 2025
Typearticle
Languageen
FieldComputer Science
TopicEvolutionary Algorithms and Applications
Canadian institutionsMemorial University of NewfoundlandMount Saint Vincent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVertex (graph theory)Degree (music)GraphMeasure (data warehouse)Simple (philosophy)Position (finance)

Abstract

fetched live from OpenAlex

Snort is a two-player game played on a simple graph in which players alternately colour a vertex such that they do not colour adjacent to their opponent’s vertex. In combinatorial game theory, the temperature of a position is a measure of the urgency of moving first. It is known that the temperature of Snort in general is infinite ( K 1 , n has temperature n ). We show that for all constants c there is a game of Snort for which the difference between the temperature and the maximum degree of the board is at least c . We do so by constructing a family of positions in which the temperature grows twice as fast as the maximum degree of the board.

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)0.003
metaresearch head score (Gemma)0.033
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.008
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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