High‐Resolution Poynting Flux Statistics From the Swarm Mission: How Much Is Being Underestimated at Larger Scales?
Bibliographic record
Abstract
Abstract Underestimation of the transfer of energy between the magnetosphere and ionosphere, the Poynting flux, is a persistent issue in space weather studies and the high‐latitude ionospheric models. Thought to be due to the inability to resolve small‐scale fluctuations of the ionospheric electric field, this underestimation could lead to significant further underestimations in parameters such as the thermospheric mass density and consequential satellite drag. Utilizing 16 Hz ion velocity and magnetic field measurements from the Swarm satellite mission, we examine the observed Poynting flux due to electric field fluctuations on very small spatial scales (∼1 km), and then artificially smooth the data to increase the observed scale. We quantify the decrease of integrated Poynting flux, poleward of 60/‐60° geomagnetic latitude, with increasing spatial scale. The decrease can be underestimated by as much as 15% by increasing scale from 1 km to only 8.6 km, or 16–2 Hz equivalent, with upward Poynting flux decreasing significantly faster. Our results thus point to a significant Alfvén wave driven component of the Poynting flux on kilometer scales. Additionally, we observe a northern hemisphere preference for increased Poynting flux, of which we examine its dependence on scale size and interplanetary magnetic field.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".