Random walk of magnetic field lines in dynamical turbulence: A field line tracing method. I. Slab turbulence
Bibliographic record
Abstract
To study the wandering of magnetic field lines is an important subject in theoretical physics. Results of field line random walk theories can be applied in plasma physics as well as astrophysics. Previous investigations are based on magnetostatic models. These models have been used in analytical work as well as in computer simulations to warrant mathematical and numerical tractability. To replace the magnetostatic model by a dynamical turbulence model is a difficult task. In the present article, a field line tracing method is used to describe field line wandering in dynamical magnetic turbulence. As examples different models are employed, namely, the plasma wave model, the damping model of dynamical turbulence, and the random sweeping model. It is demonstrated that the choice of the turbulence model has a very strong influence on the field line structure. It seems that if dynamical turbulence effects are included, Markovian diffusion can be found for other forms of the wave spectrum as in the magnetostatic model. Therefore, the results of the present paper are useful to specify turbulence models. As a further application we consider charged particle transport at early times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".