A statistical comparison of SuperDARN spectral width boundaries and DMSP particle precipitation boundaries in the morning sector ionosphere
Bibliographic record
Abstract
Abstract. Determining reliable proxies for the ionospheric signature of the open-closed field line boundary (OCB) is crucial for making accurate ionospheric measurements of many magnetospheric processes (e.g. magnetic reconnection). This study compares the latitudes of Spectral Width Boundaries (SWBs), identified in the morning sector ionosphere using the Super Dual Auroral Radar Network (SuperDARN), with Particle Precipitation Boundaries (PPBs) determined using the low-altitude Defense Meteorological Satellite Program (DMSP) spacecraft, in order to determine whether the SWB represents a good proxy for the ionospheric projection of the OCB. The latitudes of SWBs and PPBs were identified using automated algorithms applied to 5 years (1997-2001) of data measured in the 00:00-12:00 Magnetic Local Time (MLT) range. A latitudinal difference was measured between each PPB and the nearest SWB within a ±10min Universal Time (UT) window and within a ±1h MLT window. The results show that the SWB represents a good proxy for the OCB close to midnight (~00:00-02:00 MLT) and noon (~08:00-12:00 MLT), but is located some distance (~2°-4°) equatorward of the OCB across much of the morning sector ionosphere (~02:00-08:00 MLT). On the basis of this and other studies we deduce that the SWB is correlated with the poleward boundary of auroral emissions in the Lyman-Birge-Hopfield ``Long" (LBHL) UV emission range and hence, that spectral width is inversely correlated with the energy flux of precipitating electrons. We further conclude that the combination of two factors may explain the spatial distribution of spectral width values in the polar ionospheres. The small-scale structure of the convection electric field leads to an enhancement in spectral width in regions close to the OCB, whereas increases in ionospheric conductivity (relating to the level of incident electron energy flux) lead to a reduction in spectral width in regions just equatorward of the OCB.
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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 | 0.003 |
| 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 | 0.000 | 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".