Informed virtual geographic environments: a geometrically precise and semantically enriched model for multi-agent geo-simulations
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
In this paper, we propose a novel approach that extends our Informed Virtual Geographic Environment (IVGE) model in order to effectively manage knowledge about the environment and support agents' cognitive capabilities and spatial behaviours. Our approach relies on previous well established theories on human spatial behaviours and the way people apprehend the spatial characteristics of their surroundings in order to navigate and to interact with the physical world. It is also inspired by Gibson's work on affordances and knowledge provided by the environment to guide agent-environment interactions. The main contribution of our approach is to provide cognitive situated agents with: (1) knowledge about the environment represented using Conceptual Graphs (CG); (2) tools and mechanisms that allow them to acquire knowledge about the environment; and (3) the capability to reason about this knowledge and to autonomously make decisions and to act with respect to both their own and the virtual environment's characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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