Bibliographic record
Abstract
Performance analysis of systems is an important part of system evaluation. If the \nanalyzed system exists, performance analysis can be based on system's measurements \n(using some sort of instrumentation). If the analyzed system does not exist (as is \nthe case of system upgrading, improvement or design), the approach is to build a \n(mathematical) model of the system and to use this model for performance analysis. \nFor systems exhibiting concurrency, resource sharing or synchronization of activities, \nPetri nets are very often used as the modeling formalism. In colored Petri nets, \none of nontrivial tasks is to find bindings, i.e. mapping of free variables used in \narc expressions to specific colors. Bindings are needed to determine state transitions \nof a system, therefore, are needed in all analyses of system's behavior. A heuristic \napproach is proposed which enhances the efficiency of finding bindings in colored Petri \nnets. Also, performance analysis is used to compare the proposed approach with some \nother approaches to finding bindings and some remarkable improvements are shown \nthrough this analysis.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".