Toward Quantitative Measures for the Semantic Quality of Polygon Generalization
Bibliographic record
Abstract
Map generalization changes the semantics and geometry of map objects according to the context defined by users. How to evaluate and ensure the quality of generalization has become a major issue in contemporary digital cartography. The change in semantics after generalization has been studied much less than the other two aspects (geometry and topology). This research investigates the effect of generalization operations on the semantics of map objects. A set of quantitative measures for semantic change is put forward. A case study of a land-use map is implemented to illustrate the practical usefulness of these proposed measures, with a merging operation as an example of polygon generalization. The results indicate that these measures are not only sound in theory but also meaningful in practice.
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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 | 0.027 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".