Scaling laws and frequency distributions of avalanche areas in a self‐organized criticality model of solar flares
Bibliographic record
Abstract
We calculate the spreading exponents and some geometrical properties of avalanches in a novel avalanche model of solar flares, closely built on Parker's physical picture of coronal heating by nanoflares. The model is based on an idealized representation of a coronal loop as a bundle of magnetic flux strands wrapping around one another, numerically implemented as an anisotropic cellular automaton. We demonstrate that the growth of avalanches in this model exhibits power‐laws correlations that are numerically consistent with the behavior of a general class of statistical physical systems in the vicinity of a stationary critical point. This demonstrates that the model indeed operates in a self‐organized critical regime. Moreover, we find that the frequency distribution of avalanche peak areas A assumes a power‐law form f(A) ∝ with an index αA ≃ 2.45, in excellent agreement with observationally‐inferred values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".