Bibliographic record
Abstract
An approach to verification of component compatibility is proposed in which each component's behaviour (at its interfaces) is represented by a labeled Petri net in such a way that the sequences of services (provided or requested) correspond to sequences of labels assigned to occurring transitions. The behaviour of a component can thus be defined as the language of its modeling net. Two interacting components are compatible if and only if all possible sequences of services requested by one of these two components can be satisfied by the other component; in other words, two components are compatible if the language of the requesting component is a subset of the language of the component providing the services. Verification of this simple relation depends upon the class of languages defining the behaviours of the components. If the languages are regular, the verification of compatibility is straightforward. For non-regular languages, a more elaborate approach is needed in which a net model composed of the interacting components is checked for the absence of deadlocks. Some applications of the proposed approach are also discussed.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".