Identifying intervals of temporally invariant field‐aligned currents from Swarm: Assessing the validity of single‐spacecraft methods
Bibliographic record
Abstract
Abstract Field‐aligned currents (FACs) are a fundamental component of coupled solar wind‐magnetosphere‐ionosphere. By assuming that FACs can be approximated by stationary infinite current sheets that do not change on the spacecraft crossing time, single‐spacecraft magnetic field measurements can be used to estimate the currents flowing in space. By combining data from multiple spacecraft on similar orbits, these stationarity assumptions can be tested. In this technical report, we present a new technique that combines cross correlation and linear fitting of multiple spacecraft measurements to determine the reliability of the FAC estimates. We show that this technique can identify those intervals in which the currents estimated from single‐spacecraft techniques are both well correlated and have similar amplitudes, thus meeting the spatial and temporal stationarity requirements. Using data from European Space Agency's Swarm mission from 2014 to 2015, we show that larger‐scale currents (>450 km) are well correlated and have a one‐to‐one fit up to 50% of the time, whereas small‐scale (<50 km) currents show similar amplitudes only ~1% of the time despite there being a good correlation 18% of the time. It is thus imperative to examine both the correlation and amplitude of the calculated FACs in order to assess both the validity of the underlying assumptions and hence ultimately the reliability of such single‐spacecraft FAC estimates.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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".