How variable are Birkeland currents?
Bibliographic record
Abstract
Abstract I address the problem of estimating the time-rate-of-change of high-latitude Birkeland currents by using a string-of-pearls formation of satellites. Space series are calculated by linear interpolation of measurements made at the revisit times of the satellites. A lower bound on the total time derivative can be estimated as a function of distance along the orbit. Space series of the vertical component of electric current density, used as a proxy for field-aligned (Birkeland) current density at high latitude, are estimated from the along-track spatial derivative of Swarm magnetic field measurements residual to the CHAOS-7 internal field model. The results reveal non-negligible total time derivatives over periods shorter than 2 mins. Auroral Birkeland current densities derived from single-satellite traversals of magnetic field gradients can change dramatically in the time it takes a single satellite to cross a large-scale current system. In one example, during an overflight by the Swarm satellites of the THEMIS Fort Yukon all-sky imager on 1 December 2013, the vertical current density poleward of a visually quiescent auroral arc changes from $$\sim 0.3\ \mu \,\hbox {A}/\hbox {m}^{2}$$ ∼ 0.3 μ A / m 2 upward to $$\sim 1.0\ \mu \,\hbox {A}/\hbox {m}^{2}$$ ∼ 1.0 μ A / m 2 downward in 13.7 s (corresponding to an along-track separation of Swarm A and B of 104 km). The variability of Auroral Birkeland currents, between 25 November 2013 and 31 December 2013, as estimated by the median of $$|dj_z/dt|$$ | d j z / d t | , reaches $$15\ \hbox {nA}/\hbox {m}^{2}/\textrm{s}$$ 15 nA / m 2 / s in the northern dayside auroral zone and exceeds $$30\ \hbox {nA}/\hbox {m}^{2}/\textrm{s}$$ 30 nA / m 2 / s in the pre-noon sector of the southern hemisphere. Graphic Abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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 teacher head, 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".