A Statistical Survey of F2 Layer Peak Properties in the Polar Cap Ionosphere Observed by RISR-N
Bibliographic record
Abstract
This study presents a statistical survey of long-pulse incoherent scatter radar observations of the F-region ionosphere from the Resolute Bay Incoherent Scatter North (RISR-N) station between 2009 and 2025. We present an algorithm to track the altitude ($h_{mF2}$) and electron density ($N_{mF2}$) of the F2 layer peak for each one minute time step of RISR-N data by fitting an Epstein Layer Model to the electron density profiles. The fitted time series of $h_{mF2}$ and $N_{mF2}$ are compared to Empirical Canadian High Arctic Ionosphere Model (E-CHAIM) estimations to detect lifted (L) events, high-density (D) events, or lifted and high-density (LD) events. We then statistically analyze the background solar wind and geomagnetic conditions during L, D, and LD events. We observe that L events and D events have very different distributions. L events are observed most frequently during times of fast solar wind and high geomagnetic activity. These conditions are consistent with high speed stream driven storms. D events are observed most frequently during times of relatively slow solar wind and are not correlated with geomagnetic activity. D events also have a strong correlation with southward interplanetary magnetic field. L and D events also have nearly disjoint seasonal distributions. This suggests that L and D events are distinct ionospheric phenomena. LD events are relatively rare but show a preference for solar maximum conditions, whereas L and D events did not have a strong relationship with $F10.7$.
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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 | 0.001 |
| 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) | 0.000 | 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".