An approach for generating state machine designs from scenarios
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
A new approach for generating state machine designs from scenarios is presented that assigns state values to the states of system's components (processes) in different scenarios. State values are assigned using a light domain theory inferred from the domain knowledge, and are used to detect identical states of processes in order to merge partial behaviours of scenarios. The domain theory will be systematically constructed by requesting the domain expert to look at some tables that their rows and columns are filled with selected messages from scenarios, and possibly finds one cell that its column has a special relation with its row called semantical causality. Semantical causality captures an invariant property of a system in terms of performance dependability between messages and as a part of the domain knowledge that is not explicitly defined in a scenario. Furthermore, to detect emergent behaviours in the generated state machines, non-deterministic behaviour of processes is defined to charactrize the conditions that emergent behaviours are allowed by systems's architecture defined by scenarios.
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 | 0.001 | 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