Field Verification in the Scientist–AI System: From Pathological Uncertainty to Quality Data

Abstract

A structured information system does not eliminate uncertainty; it identifies uncertainty and potentially corrective observations. The methodological challenge is converting a heterogeneous information field into representative empirical evidence.

Field verification cannot mean sampling wherever access is available. Access to a site is a practical condition, not a research design. Representative observation requires knowledge of the process and selection of locations where it can be detected. Because long-term war-related environmental contamination remains insufficiently understood, the representativeness of many existing sampling sites cannot be demonstrated.

Each field observation should test a relationship, distinguish competing hypotheses, establish a baseline, measure a transition, or reveal temporal development. Site selection must follow from this purpose and a process model. The proposed system combines reference stations, verification sites, experimental polygons, repeated observations, and pilot studies.

The expert–AI system can compare sites, integrate terrain, hydrological, geological, historical, military, and land-use information, model transport pathways, and identify observations with discriminatory value. AI cannot certify representativeness or replace sampling and laboratory analysis. Results should be called representative only when the process, site rationale, spatial and temporal scales, and extrapolation limits are explicitly documented; otherwise, they remain exploratory or local.