Bibliographic record
Abstract
The concept of fractals is being widely used in several cartographic procedures such as line enhancement, surface generation, generalisation, interpolation and error esti mation. Such applications rely on the estimation of the fractional dimension (D) of lines and surfaces. Measuring D for surfaces can be achieved from contours and profiles extracted from the surface or from the variability of the surface taken as a whole. In a fractal and self-similar terrain, the values of D should be in agreement regardless of the method used. Mark and Aronson (1984) applied the variogram technique to DEM and observed sharp changes in D with scale suggesting that terrains are composed of nested structures with a highly disorganised and complex v compo nent (D=2.6) in the long range and a smooth component (D= 2.2) in the short range. The high dimensions may not re flect the terrain itself but be the result of combining residual anisotropic effects at long distances. Tests per formed on DEM (or portions of DEM) show that the short range dimensions of the surface variogram are consistent with those extracted from profiles and contours (2.0-=D< 2.3). Systematic variations of D with altitude and loca tion were also observed indicating a lack of self-similar ity in spite of the apparent self-similarity of the sur face variogram.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.547 | 0.174 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".