reaalsele

Reaalsele is a coined term used in speculative discussions and lightweight educational settings to denote a mechanism for selecting feasible real-valued outcomes from a computational model by enforcing a realism constraint. The name blends real and selection to signal that only outcomes compatible with real-world feasibility are retained.

Conceptually, reaalsele operates as a post-processing step on a set of candidate results produced by a model. After generation, each candidate is evaluated against a realism criterion reflecting basic physical or logical constraints, such as nonnegativity, mass balance, or conservation laws. Candidates that satisfy the criterion are kept; those that do not are adjusted or discarded in favor of feasible alternatives.

In practice, the realism criterion can be implemented through threshold checks, optimization adjustments, or propagation of constraints across related variables. The exact formulation is not standardized, and different authors may propose varying rules depending on domain, such as engineering, economics, or environmental science. Because reaalsele is not an established field, descriptions of its methods are often example-based rather than universal.

Applications described in fictional or educational contexts include ensuring outputs respect physical limits in climate or engineering simulations, or guiding decision-support tools to avoid impossible resource allocations. In real-world work, similar concepts appear under feasibility analysis, constraint satisfaction, or model validation. Limitations include subjectivity in choosing realism criteria and potential bias introduced during post-processing.

See also: realism constraint, feasibility, constraint satisfaction, model validation, post-processing.