A Meta-ontology Framework for Parameter Concepts of Disease Spread Simulation Models
Bibliographic record
Abstract
Over the past decade, many complex simulation models have been developed. However, the formal semantics of the many parameters used by these models and their relationship to documented domain knowledge are often overlooked. Therefore, one of the limitations of these simulation models is their lack of formal semantics and definition of the parameters. This can lead to issues such as problems with updating these models as more or different knowledge enters the domain, complexity with running comparisons of models that claim to be simulating the same domain, and overall complexity for users when they come to setting the parameters in these simulations. In this research, we propose a new approach for parameter semantics and relationship to the documented domain knowledge. An ontology has been developed as a formal semantic model for the parameters. This work reports on a novel ontological organization that separates domain knowledge from knowledge about parameters in different comparablesimulation models and formalizes a relationship between parameters by linking to the domain knowledge part of the ontological structure. This offers several advantages such as allowing explicit domain knowledge representation and provenance, allowing for the assessment of parameters with respect to domain knowledge, and assisting in the transformation of sets of parameters for comparison tasks between models. The ontology allows views about parameters to be captured. This is important because it establishes a neutral view point which allows the certainty assessment of parameter semantics to documented domain knowledge. This work also acknowledges the limitations in ontology creation. It is a time consuming process that requires a lot of effort, and collaboration of a number of experts in different domains. While this work uses the domain of animal disease spread, the principles of ontological representation of model parameters is applicable to a wide range of domains.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".