Reconnection in weakly stochastic B-fields in 2D
Bibliographic record
Abstract
The idea that there could be some way of producing fast magnetic reconnection even in highly conducting fluids is not new (Moffat 1978; Krause & Radler 1980), but early models of reconnection (see Parker 1957; Sweet 1958, e.g.) using realistic astrophysical temperatures and densities gave a very slow reconnection rate, ∼ VAS −1/2, where S ≡ η/(VAL) is the Lundquist number, η is the resistivity, and L is the size of the current sheet. It was Petschek (1964) who for the first time introduced a model for fast reconnection with a rate proportional to (log S) −1. Subsequent numerical simulations and theoretical analyses have shown that the Petschek reconnection rate is only attainable in very restricted circumstances. For instance, a modified version can stably persist in a collisionless plasma (see Drake et al. 2006a, e.g.). This means that the length of the current sheet should not exceed approximately 50 electron mean free paths (Uzdensky 2006; Yamada et al. 2006). This condition cannot be satisfied in many astrophysical environments, e.g. in the interstellar medium (Vishniac & Lazarian 1999). In a collisional plasma the X-point region required for Petschek reconnection will collapse to the Sweet-Parker geometry for large S (Biskamp 1996). The failure of the Petschek model has increased interest in the role of turbulence in reconnection. This interest has been further stimulated by the fact that the turbulence is ubiquitous in astrophysical environments where reconnection occurs, e.g. the ISM, stars, the Sun and accretion disks (Ruzmaikin et al. 1988). The idea that turbulence can affect reconnection has a long history, although usually studied in two-dimensions (2D) (Priest & Forbes 2000). Several researchers have approached this problem
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".