Toward Self-Generalizing Objects and On-the-Fly Map Generalization
Why this work is in the frame
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Bibliographic record
Abstract
Map generalization is a complex task that sometimes requires human intervention. In order to support such a process on the fly, we propose a generalization approach based on self-generalizing objects (SGOs) that encapsulate geometric patterns (forms common to several cartographic features), generalization algorithms, and spatial integrity constraints. During a database enrichment process, an SGO is created and associated with a cartographic feature or a group of features. Each SGO created is then transformed into a software agent (SGO agent) in a multi-agent on-the-fly map-generalization system. SGO agents are equipped with behaviours that enable them to coordinate the generalization process. This article presents the concept of the SGO and two prototypes developed to support this approach: a prototype for the creation of SGOs and another for the on-the-fly map generalization (which uses the created SGOs).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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