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
Abstraction is one of the most effective approaches to improving the applicability and the scalability of model-checking. The goal of abstraction is to construct a model which is small enough to analyze, yet contains enough detail to allow conclusive analysis of properties of interest. For a given concrete model, the size of its smallest possible abstraction is intimately related to the set of temporal properties preserved by the abstraction. Thus, smaller abstractions are possible if we reduce this set, for example, by disallowing the use of the next-time operator. In this paper, we improve the conclusiveness and efficiency of the 3-valued abstraction framework. We start by proposing a number of simulation relations that preserve true properties expressed in subsets of CTL without the next-time operator. We show how these simulation relations are extended into refinement relations for defining 3-valued abstractions. Using these refinement relations, we give a new abstraction method that results in more conclusive abstract models.
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 | 0.001 |
| 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