Development of Flux Imbalances in Solar Activity Nests and the Evolution of Filament Channels
Bibliographic record
Abstract
Bipolar active regions tend to emerge in tight clusters that persist at the same location in so-called activity nests. This study examines how flux evolves inside three different arrangements of interacting nests. Each contains ~2 × 10 23 Mx, and each develops local flux imbalances that interact to form filaments and filament channels. They include: a pair of isolated closely packed nests; a pair of widely spaced nests with neighboring nests on their outer flanks; and a chain of three closely packed nests. These cases result in flux imbalances that are, respectively: large and concentrated on the outer edges of the nests; large and concentrated between the nests; and weak and concentrated on the outer edges of the nests. An amount of flux equivalent to a single large sunspot pair, but composed entirely of weaker flux densities (<∣ 50∣ G), is representative of the net fluxes measured for all three examples of multiple activity nests. In the majority of cases, the pools of net flux form filament channels, i.e., configurations with a clear horizontal component of the magnetic field directed along a polarity inversion line (PIL). This study proposes that large quiescent filaments and their channels are natural storehouses of magnetic energy constructed by surface flows out of slowly reconnecting pools of "orphaned" magnetic flux that originate at outer boundaries of decaying activity nests.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".