Supergranulation as an Emergent Length Scale
Bibliographic record
Abstract
We have developed an n -body diffusion-limited aggregation model to simulate the dispersal and interaction of small-scale magnetic elements at the solar surface. The model is highly simplified and is based on the observation that small-scale magnetic elements are passively advected by the granular flow, which we approximate as a random walk. With a great many magnetic elements executing this random walk simultaneously, collisions are inevitable. We assume that these collisions lead to the aggregation of the colliding magnetic elements (if they have the same polarity) or mutual cancellation (if opposite polarity). In a similar fashion, the resulting clusters can subsequently interact with more magnetic elements or with other clusters. The clusters also undergo a random walk. However, the step size is reduced and the lifetime is increased in order to mimic the observation that larger magnetic flux concentrations move slower and live longer than smaller ones. The essential finding is that this process can produce a spatial distribution of clusters comparable to the supergranule cell pattern (depending on model parameters). The characteristic length scale associated with the spatial distribution of the clusters is quite sensitively dependent on the injection rate of fresh magnetic elements—when the injection rate is high (low) the length scale is small (large). This property provides a natural explanation for the observation that supergranule cells tend to be smaller when and where the level of magnetic activity is higher. We also find that at length scales similar to supergranulation the dominance of a given polarity tends to be enhanced, in comparison to the case where the same clusters are situated randomly in space. This is potentially testable by observation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".