Design and Conceptual Framework of ARGOS: An AI-Assisted System for Managing Georeferenced Environmental Surveys using UAS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it