Using RISR‐N to Resolve Variations in Dayside and Nightside Plasma Density Spatial‐Scales
Bibliographic record
Abstract
Abstract To provide new insights into plasma density scale‐sizes in the polar cap, irregularity spectra are developed and tracked relative to magnetic local time (MLT) and solar zenith angle (SZA). A novel Incoherent Scatter Radar (ISR) technique is applied to develop spectra between 20 and 300 km using 2016 to 2018 imaginglp mode data from Resolute Bay ISR‐North. This technique leverages: (a) volumetric plasma density measurements from Advanced Modular ISRs, (b) the slow F‐region cross‐field plasma diffusion at scales greater than 10 km, and (c) that high‐latitude geomagnetic field lines are nearly vertical. The results of this work find that the largest spectral features within periodograms that use sunlit or dayside plasma densities are predominately above 100 km, indicating that structures that are above 100 km are more common than structures below 100 km in dayside/sunlit plasma. However, the opposite is true when plasma is in the dark or on the nightside, where the largest spectral features are predominately below 100 km. This contrast between the dayside and nightside is symptomatic of photoionization generating structures larger than 100 km, highlighting the role of photoionization or E‐region shorting in removing structures less than 100 km or driving larger scale‐structures more strongly, and the role of other mechanisms (such as flows, recombination, precipitation, and instabilities) in generating small‐scale structures. This paper will discuss these findings in detail, as well as discuss forthcoming works.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".