Vector Planar Elements: Geometrical Similarity Measurement Based On Fourier Descriptors
Bibliographic record
Abstract
With the rotation, translation and scaling invariance, etc. it is difficult to measure the similarity between GIS planar elements. To describe the graphics precisely, according to the number of shortest paths where vertices occur, we define the “vertex betweenness;” this measures the importance of each vertex in a graph. The higher the vertex betweenness, the more important vertex becomes. We propose a contour fea ture points extraction method, where Fourier descriptors are used. We normalize the first n order factors of Fourier descriptors, on the basis of similarity between polygons, which is obtained by comparing the cosine values for every two vectors. The experiment is operated on two different data scales, 1:50 000 and 1:250 000. Combined with analysis of impact factors during similarity measurement, the experiment results show that the contour feature points extraction method can effectively measure the geometrical similarity between GIS planar elements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".