A New Approach for the Simplification of Contours
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
The requirements for the simplification of contours are explained, and existing approaches for the generalization (i.e., simplification and smoothing) of contours are briefly summarized. Skeleton lines (i.e., drainage and ridge lines) are supposed to provide information for the determination of characteristic parts of contours. Characteristic points are automatically determined during the process of deriving skeleton lines from contours in accordance with the method developed by Aumann, Ebner, and Tang (1991). Three widely used algorithms for the simplification of contours - nth point, distance tolerance, and Douglas-Peucker - are examined. They are analysed with respect to the retention of characteristic parts of contours, based on case studies. Finally, the algorithms are modified in such a way as to consider the determined characteristic points. A new simplification criterion is included in the algorithms, ensuring that they retain the characteristic parts of contours.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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