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Resilient algorithms in high-performance computing are subject to rigorousnon-functional constraints. Resiliency must not increase the runtime, memoryfootprint or I/O demands too significantly. We propose a task-based soft errordetection scheme that relies on error criteria per task outcome. They formalisehow ``dubious'' an outcome is, i.e. how likely it contains an error. Our wholesimulation is replicated once, forming two teams of MPI ranks that share theirtask results. Thus, ideally each team handles only around half of the workload.If a task yields large error criteria values, i.e.~is dubious, we compute thetask redundantly and compare the outcomes. Whenever they disagree, the taskresult with a lower error likeliness is accepted. We obtain a self-healing,resilient algorithm which can compensate silent floating-point errors without asignificant performance, I/O or memory footprint penalty. Case studies howeversuggest that a careful, domain-specific tailoring of the error criteria remainsessential.

Doubt and Redundancy Kill Soft Errors -- Towards Detection and Correction of Silent Data Corruption in Task-based Numerical Software