Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Shuffle on trajectories was introduced by Mateescu et al. [147] as a method of generalizing several studied operations on words, such as the shuffle, concatenation and insertion operations. This natural construction has received significant and varied attention in the literature. In this thesis, we consider several unexamined areas related to shuffle on trajectories. We first examine the state complexity of the shuffle on trajectories. We find that the density of the set of trajectories is an appropriate measure of the complexity of the associated operation, since low density sets of trajectories yield less complex operations. We introduce the operation of deletion along trajectories, which serves as an inverse to shuffle on trajectories. The operation is also of independent interest, and we examine its closure properties. The study of deletion along trajectories also leads to the study of language equations and systems of language equations with shuffle on trajectories. The notion of shuffle on trajectories also has applications to the theory of codes. Each shuffle on trajectories operation defines a class of languages. Several of these language classes are important in the theory of codes, including the prefix-, suffix-, biprefix-codes and the hypercodes. We investigate these classes of languages, decidability questions, and related binary relations. We conclude with results relating to iteration of shuffle and deletion on trajectories. We characterize the smallest language closed under shuffle on trajectories or deletion along trajectories, as well as generalize the notion of primitive words and primitive roots. Further examination of language equations are also possible with the iterated counterparts of shuffle and deletion along trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it