A Hybrid GraphRAG Framework for Geospatial Contextualization in Decision Support Systems
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
Large Language Models (LLMs) face limitations in specialized domains such as environmental management due to hallucinations and static knowledge. Standard Retrieval-Augmented Generation (RAG) provides contextualization through vector search but struggles with complex queries requiring structured reasoning and explainability—crucial in geospatial contexts where “where” matters. We propose a hybrid GraphRAG architecture that combines vector retrieval with a dynamically constructed Knowledge Graph (KG). By enabling LLMs to extract and link entities (e.g., regulations, project sites) within the KG, our approach enhances both semantic flexibility and structured reasoning, improving accuracy for spatially aware and regulatory queries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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