Bibliographic record
Abstract
Abstract. Traditional fractal image coding seeks to approximate an im-age function u as a union of spatially-contracted and greyscale-modied copies of itself, i.e., u Tu, where T is a contractive fractal transform operator on an appropriate space of functions. Consequently u is well approximated by u, the unique xed point of T, which can then be con-structed by the discrete iteration procedure un+1 = Tn. In a previous work, we showed that the evolution equation yt = Oy y produces a continuous evolution y(x; t) to y, the xed point of a con-tractive operator O. This method was applied to the discrete fractal transform operator, in which case the evolution equation takes the form of a nonlocal dierential equation under which regions of the image are modi ed according to information from other regions. In this paper we extend the scope of this evolution equation by intro-ducing additional operators, e.g., diusion or curvature operators, that \\compete " with the fractal transform operator. As a result, the asymp-totic limiting function y1 is a modication of the xed point u of the original fractal transform. The modication can be viewed as a replace-ment of traditional postprocessing methods that are employed to \\touch up " the attractor function u. 1
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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 | 0.000 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".