Title Field Verification in the Scientist–AI System: From Pathological Uncertainty to Quality Data
Author(s) Laboratory of Military Ecotones
Affiliation New Euro Vision: exhibitions, marketing, research s.r.o.
Country Czech Republic
Contribution type conceptual paper; methodological paper
Thematic area • Theory, Methodology, and Evidence
Conference framework connection • Analytical Track AT-01 — provisional title to be defined• Analytical Track AT-02 — provisional title to be defined• Analytical Track AT-03 — provisional title to be defined• Analytical Track AT-04 — provisional title to be defined• Analytical Track AT-05 — provisional title to be defined• Analytical Track AT-06 — provisional title to be defined• Analytical Track AT-07 — provisional title to be defined
Abstract A structured information system does not eliminate uncertainty. It identifies which claims remain uncertain, why they are uncertain, and which observations may reduce that uncertainty. The next methodological problem is therefore the transition from a heterogeneous information field to a limited body of representative empirical evidence.
Field verification cannot be defined as sampling wherever access happens to be available. Access to an archive, territory, water body, or soil site is a practical condition, not a research design. In field-based sciences, representative observation traditionally depends on knowledge of the natural process and expert selection of locations where that process can be observed. Research on war-related environmental contamination currently faces an additional difficulty: the long-term process itself remains insufficiently understood. Consequently, the representativeness of many existing sampling locations cannot yet be demonstrated.
The first task is to formulate the problem precisely. A field observation should test a specified relationship, distinguish between competing hypotheses, establish a baseline, measure a transition, or reveal the temporal development of a process. The selection of a location must follow from this purpose and from an explicit model of the expected environmental process.
The proposed observation system combines reference stations, verification sites, experimental polygons, repeated observations, and targeted pilot studies. Reference stations support the identification of background conditions and long-term change. Verification sites examine claims or hypotheses derived from existing information. Experimental polygons allow controlled comparison of processes, methods, and indicators. Pilot studies test whether a larger observation design is justified.
Artificial intelligence is integrated throughout this system. The expert–AI configuration can compare candidate sites, combine terrain, hydrological, geological, historical, military, and land-use information, identify contradictions, model alternative transport pathways, and determine which observations have the greatest discriminatory value. AI does not certify representativeness and does not replace sampling or laboratory analysis. The expert remains responsible for the process model, site selection, method, and interpretation.
A strict requirement follows: no field result should be presented as representative without an explicit explanation of the process represented, the reason for selecting the location, the relevant spatial and temporal scale, and the limits of extrapolation. Where current knowledge cannot support such a claim, the study must be identified as exploratory or local.
The goal is not to collect the maximum number of samples. It is to create a small, cumulative, and revisable system of high-quality observations capable of improving recognition of long-term war-related environmental processes.
Key points • Available access does not by itself provide a scientifically justified observation location.
• Representative site selection requires an explicit understanding of the process being investigated.
• Current knowledge of long-term war-contamination processes remains insufficient for many broad claims of representativeness.
• Reference stations, verification sites, experimental polygons, and pilot studies perform different methodological functions.
• AI can improve observation design, but the expert remains responsible for site selection, empirical method, and interpretation.
Keywords field verification; scientist–AI system; war-related environmental contamination; representative observation; reference stations; experimental polygons; sampling design; quality data; freshwater systems; military ecotones
Main discussion question How can a limited field observation system be designed to reduce the most important uncertainties and produce representative evidence about long-term war-related environmental processes?
OJS publication link https://pollution-diseases-ojs.org/index.php/pd/article/view/79
Note. Analytical TracksIn addition to the main thematic areas, the conference programme will include several cross-cutting analytical tracks. These tracks will be defined during the preparation of the programme, based on the submitted abstracts and the emerging links between presentations.At the preliminary stage, abstracts may be assigned to provisional analytical tracks marked as AT-01 to AT-07. Final track titles will be announced after the Scientific Committee has reviewed the submitted materials.