A map generalization model based on algebra mapping transformation
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Bibliographic record
Abstract
From the point of view of mapping transformation, this paper presents a map generalization conceptual framework which regards generalization as two kinds of mapping procedures: spatial entity mapping and spatial relationship mapping. According to the number of changes in the participating entities, spatial entity mapping is classified as 1-1, n-1, n-m mapping. Spatial relationship mapping is described as a composite relationship transformation of the components: topology, distance and orientation. The concept 'spatial relationship resolution' is introduced to describe spatial relationship related constraints. Based on the 9 intersection model, the cardinal direction model and the iso-distance-relationship model, the paper gives three sorts of relationship resolution representations for topological, distance and orientation relationship respectively. The behavior of the two mappings in map generalization is discussed and the spatial relationship abstraction obtains emphasis compared with the traditional generalization conceptual model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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