Graphs whose \(l_p\)-optimal rankings are \(l_{\infty}\) Optimal
Bibliographic record
Abstract
A ranking on a graph G is a function f : V ( G ) → { 1 , 2 , … , k } with the following restriction: if f ( u ) = f ( v ) for any u , v ∈ V ( G ) , then on every u v path in G , there exists a vertex w with f ( w ) > f ( u ) . The optimality of a ranking is conventionally measured in terms of the l ∞ norm of the sequence of labels produced by the ranking. In \cite{jacob2017lp} we compared this conventional notion of optimality with the l p norm of the sequence of labels in the ranking for any p ∈ [ 0 , ∞ ) , showing that for any non-negative integer c and any non-negative real number p , we can find a graph such that the sets of l p -optimal and l ∞ -optimal rankings are disjoint. In this paper we identify some graphs whose set of l p -optimal rankings and set of l ∞ -optimal rankings overlap. In particular, we establish that for paths and cycles, if p > 0 then l p optimality implies l ∞ optimality but not the other way around, while for any complete multipartite graph, l p optimality and l ∞ optimality are equivalent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".