Value-Peaks of Permutations
Bibliographic record
Abstract
In this paper, we focus on a "local property" of permutations: value-peak. A permutation $\sigma$ has a value-peak $\sigma(i)$ if $\sigma(i-1) < \sigma(i)>\sigma(i+1)$ for some $i\in[2,n-1]$. Define $VP(\sigma)$ as the set of value-peaks of the permutation $\sigma$. For any $S\subseteq [3,n]$, define $VP_n(S)$ such that $VP(\sigma)=S$. Let ${\cal P}_n=\{S\mid VP_n(S)\neq\emptyset\}$. we make the set ${\cal P}_n$ into a poset $\mathfrak{ P}$$_n$ by defining $S\preceq T$ if $S\subseteq T$ as sets. We prove that the poset $\mathfrak{ P}$$_n$ is a simplicial complex on the set $[3,n]$ and study some of its properties. We give enumerative formulae of permutations in the set $VP_n(S)$.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".