Design and Conceptual Framework of ARGOS: An AI-Assisted System for Managing Georeferenced Environmental Surveys using UAS
Bibliographic record
Abstract
This paper introduces the conceptual design of ARGOS (Advanced Retrieval of Georeferenced Observational Surveys), an AI-assisted framework for managing, analysing, and querying environmental data acquired by Unmanned Aerial Systems (UAS). ARGOS is being developed to address the current lack of integrated systems capable of indexing, interpreting, and retrieving large volumes of heterogeneous, georeferenced drone imagery in a traceable and intelligent manner. Building on a robust scientific and technical foundation, the proposed architecture includes a modular Data Management System (DMS), a metadata tagging and classification protocol, and a multi-agent AI validation layer interacting with non-proprietary large language models (LLMs). The system design prioritizes explainability, interoperability, and long-term scalability. Although still in the early development phase, ARGOS is structured to support future applications such as anomaly detection and environmental change tracking by combining structured data organization through its internal DMS, intelligent metadata tagging, explainable AI-based querying via external LLMs, and real-time multi-agent consistency checks. These components are designed to operate across diverse spatial and temporal datasets, enabling advanced analysis and transparent knowledge extraction in geophysical and environmental monitoring contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".