Topside Electron Density Representations for Middle and High Latitudes: A Topside Parameterization for E‐CHAIM Based On the NeQuick
Bibliographic record
Abstract
Abstract In this study, we present a topside model representation to be used by the Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM). In the process of this, we also present a comprehensive evaluation of the NeQuick's, and by extension the International Reference Ionosphere's, topside electron density model for middle and high latitudes in the Northern Hemisphere. Using data gathered from all available incoherent scatter radars, topside sounders, and Global Navigation Satellite System Radio Occultation satellites, we show that the current NeQuick parameterization suboptimally represents the shape of the topside electron density profile at these latitudes and performs poorly in the representation of seasonal and solar cycle variations of the topside scale thickness. Despite this, the simple, one variable, NeQuick model is a powerful tool for modeling the topside ionosphere. By refitting the parameters that define the maximum topside scale thickness and the rate of increase of the scale height within the NeQuick topside model function, r and g, respectively, and refitting the model's parameterization of the scale height at the F region peak, H0, we find considerable improvement in the NeQuick's ability to represent the topside shape and behavior. Building on these results, we present a new topside model extension of the E‐CHAIM based on the revised NeQuick function. Overall, root‐mean‐square errors in topside electron density are improved over the traditional International Reference Ionosphere/NeQuick topside by 31% for a new NeQuick parameterization and by 36% for a newly proposed topside for E‐CHAIM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".