Bibliographic record
Abstract
This article presents a vector shape generalization extension for the ArcView GIS called VECTORGEN. The user specifies a shape file of "black" polygons and selects one of two complementary routines for generalization of the black-white pattern. The Alpha routine (A-GEN) deletes islands of both black and white smaller than a threshold of alpha units, while the Epsilon routine (E-GEN), a more complex procedure, is the focus of this article. Epsilon generalization performs a variety of tasks simultaneously, although its main purposes are deleting narrow areas (of both black and white) and smoothing the bounding line. In addition, it deletes smaller areas and aggregates nearby like-coloured areas. It employs a new algorithm (Vector ESSE) that marries Perkal's rolling disk concept with the operational simplicity of raster extend-shrink blankets. The user specifies a buffer width epsilon (ε), and the technique ensures that all turning radii on the smoothed bounding line are at least 1ε in radius, all black or white patches are at least 2ε wide, and all patches are large enough to contain a circle of radius 1ε. Examples of the use of both alpha and epsilon routines are provided, with a discussion of their advantages and disadvantages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".