The Möbius function of generalized subword order
Bibliographic record
Abstract
Let $P$ be a poset and let $P^*$ be the set of all finite length words over $P$. Generalized subword order is the partial order on $P^*$ obtained by letting $u≤ w$ if and only if there is a subword $u'$ of $w$ having the same length as $u$ such that each element of $u$ is less than or equal to the corresponding element of $u'$ in the partial order on $P$. Classical subword order arises when $P$ is an antichain, while letting $P$ be a chain gives an order on compositions. For any finite poset $P$, we give a simple formula for the Möbius function of $P^*$ in terms of the Möbius function of $P$. This permits us to rederive in an easy and uniform manner previous results of Björner, Sagan and Vatter, and Tomie. We are also able to determine the homotopy type of all intervals in $P^*$ for any finite $P$ of rank at most 1. Soit $P$ un ensemble partiellement ordonné et soit $P^*$ l'ensemble des mots de longueur finie sur $P$. On définit l'ordre des sous-mots généralisé comme l'ordre partiel sur $P^*$ obtenu en posant $u≤ w$ s'il existe un sous-mot $u'$ de $w$ ayant la même longueur que $u$, tel que chaque élément de $u$ soit plus petit ou égal à l'élément correspondant de $u'$ dans l'ordre partiel sur $P$. L'ordre des sous-mots classique correspond au cas où $P$ est une antichaîne ; tandis que si P est une chaîne, on obtient un ordre sur les compositions. Pour tout ensemble partiellement ordonné fini $P$, nous donnons une formule simple pour la fonction de Möbius de $P^*$ en fonction de celle de $P$. Cela nous permet de retrouver de manière simple et uniforme des résultats de Björner, Sagan et Vatter, et de Tomie. Nous sommes aussi en mesure de déterminer le type d'homotopie de tous les intervalles de $P^*$ pour n'importe quel $P$ fini de rang au plus 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".