Bibliographic record
Abstract
Graph burning is a combinatorial game or process that models the spread of in \nuence \nthroughout a network. We introduce a generalization of graph burning which applies to \nhypergraphs. One of our key results is that arbitrary hypergraphs do not satisfy a bound \nanalogous to the one in the Burning Number Conjecture. We also introduce a variant called \n\\lazy" hypergraph burning, along with a new parameter, the lazy burning number. Interestingly, \nlazily burning a graph is trivial, while lazily burning a hypergraph can be quite \ncomplicated. Moreover, the lazy burning model is a useful tool for analyzing the roundbased \nmodel. We obtain bounds on the burning number and lazy burning number of a \nhypergraph in terms of its parameters, as well as stronger bounds that apply to Steiner \ntriple systems. We also discuss the complexity of both the round-based and lazy models. \nFinally, we introduce two further variants of (lazy) hypergraph burning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".