Title The Scientist–AI Research System: An Integrated Working Standard for War-Related Environmental Contamination
Author(s) Dmitry Nikolaenko
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 This contribution integrates the methodological positions developed in six preceding papers into a single working standard for research on war-related environmental contamination. The purpose is not to formulate a universal model of science or artificial intelligence. It is to state explicitly how the Laboratory of Military Ecotones and the Laboratory of Information Systems will organize their cognitive and empirical work.
The standard begins with the real environmental problem rather than with an available discipline, dataset, institution, or method. War-related contamination is treated as a developing process connecting military events, damaged infrastructure, post-war management, environmental transformation, soil and freshwater transport, ecological exposure, human activity, and delayed consequences. Individual disciplinary studies can contribute essential knowledge without representing this process as a whole.
The operative cognitive unit is the scientist–AI system. The human expert defines the object, formulates the problem, evaluates evidence, recognizes physically or conceptually unacceptable results, selects verification procedures, and accepts responsibility for the final decision. AI expands the capacity to search, translate, classify, compare, connect, and reformulate heterogeneous information. Its system, version, and functional contribution remain documented.
The integrated research cycle consists of problem formulation, evidence classification, generation of competing hypotheses, identification of discriminating observations, field verification, interpretation, model revision, and formulation of the next research problem. Observed, Documented, Reconstructed, Inferred, Predicted, and Verified information remain distinguishable throughout the cycle. Institutional recognition does not substitute for logical evaluation, and disciplinary admissibility does not determine whether a hypothesis may enter analysis.
Authorship is defined through responsible human judgement, disclosure of the AI research configuration, evidence provenance, and an explicit statement of the work’s contribution to the problem. Responsibility is documented through an archival interaction record and a concise Logical Record of Expert–AI Problem Solving. Empirical work is designed to reduce specified uncertainties rather than merely accumulate available samples.
The two laboratories perform complementary functions. The Laboratory of Military Ecotones defines and investigates war-transformed environmental systems. The Laboratory of Information Systems preserves evidence status, provenance, uncertainty, analytical connections, hypothesis histories, and the cognitive record of AI-assisted research.
This system remains open to correction and alternative expert standards. It does not require prior institutional recognition. Its adequacy will be evaluated through the transparency of its procedures, the quality of its empirical verification, and its demonstrated ability to improve recognition and resolution of real problems affecting war-transformed territories, soils, and freshwater systems.
Key points • The research process begins with the real environmental problem rather than with disciplinary or institutional availability.
• The scientist–AI system is the operative cognitive unit, while final scientific responsibility remains human.
• Evidence classification, competing hypotheses, discriminating observations, and field verification form one continuous research cycle.
• Authorship, responsibility, uncertainty, AI assistance, and significant information transformations must remain explicit.
• The two laboratories provide complementary scientific and information functions within a single working system.
Keywords scientist–AI system; integrated research standard; war-related environmental contamination; military ecotones; information systems; evidence status; hypothesis generation; field verification; scientific responsibility; freshwater systems
Main discussion question Can an explicit scientist–AI research cycle transform fragmented information and isolated observations into a cumulative, verifiable understanding of long-term war-related environmental contamination?
OJS publication link https://pollution-diseases-ojs.org/index.php/pd/article/view/80
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.