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Provenance and Uncertainty
IncidentGraph treats provenance, contradiction and missingness as first-class parts of the output rather than optional explanations added after inference.
Accepted event edges are required to contain supporting evidence references. This keeps a path from a graph claim back to the camera/source observations that contributed to it.
If a sensor is unavailable over an interval, IncidentGraph records a missing-evidence object and leaves affected transitions unresolved.
Not observed ⇒ unknown, not did not occur.
This prevents a common logical error in sparse or asynchronous camera networks.
Competing claims are grouped. The selected claim can be identified for downstream use, but losing subjects, scores and proposals remain available for audit.
The activity-recognition study reports expected calibration error (ECE) and multiclass Brier score. Temperature scaling is used as a post-hoc classifier calibration baseline.
The controlled benchmark also evaluates association-confidence calibration. Its aggregate diagnostic graph score includes entity/event quality, temporal order, contradiction handling, missing-evidence handling, provenance coverage and calibration.
Warning
The aggregate graph score is an engineering diagnostic average, not a learned or validated decision utility for deployment.
IncidentGraph · provenance preserved · uncertainty explicit · missing evidence stays unknown · consequential use requires human review