Exploring agent-supported simulation brokering on the semantic web: foundations for a dynamic composability approach
Bibliographic record
Abstract
Federated simulations address the need for interoperability, as well as the improvement of reuse and composability. The focal goal in a federated simulation is to facilitate composable simulations by standardizing interfaces to assure technical interoperability among disparate simulations. Yet, existing federated simulation infrastructures neither facilitate substantive interoperability nor are dynamically extensible. Emergent web services technologies hold out the potential to significantly improve the development of interoperable, extensible, and dynamically composable federations. As such, recent initiatives (i.e., XMSF) are urging the use of open standards that can be applied within an extensible framework for next generation modeling and simulation applications. We discuss how the realization of multimodel and multisimulation formalisms in terms of semantic web and agent technologies may bring new vistas to demonstrate runtime model discovery, instantiation, composition, and interoperation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".