Words Avoiding a Reflexive Acyclic Relation
Bibliographic record
Abstract
Let ${\cal A}\subseteq {\bf [n]}\times{\bf [n]}$ be a set of pairs containing the diagonal ${\cal D} = \{(i,i)\,|\, i=1,\ldots,n\}$, and such that $a\leq b$ for all $(a,b) \in {\cal A}$. We study formulae for the generating series $F_{\cal A} ({\bf x}) = \sum_w {\bf x}^w$ where the sum is over all words $w \in {\bf [n]}^*$ that avoid ${\cal A}$, i.e., $(w_i,w_{i+1})\notin {\cal A}$ for $i=1,\ldots,|w|-1$. This series is a rational function, with denominator of the form $1-\sum_{T}\mu_{{\cal A}}(T){\bf x}^T$, where the sum is over all nonempty subsets $T$ of $[n]$. Our principal focus is the case where the relation ${\cal A}$ is $\mu$-positive, i.e., $\mu_{\cal A}(T)\ge 0$ for all $T\subseteq {\bf [n]}$, in which case the form of the generating function suggests a cancellation-free combinatorial encoding of words avoiding ${\cal A}$. We supply such an interpretation for several classes of examples, including the interesting class of cycle-free (or crown-free) posets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".