A Bottomside Parameterization for the Empirical Canadian High Arctic Ionospheric Model
Bibliographic record
Abstract
Abstract In this study, we present a bottomside model representation to be used by the Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM). This model features a new approach to modeling the bottomside electron density; namely, instead of modelling electron density directly, E‐CHAIM models the altitude profile of the scale thickness of a single bottomside layer. In this approach, the curvature in the bottomside associated with the E region and F1 layer is represented in the scale thickness domain as a peak function centered at the layer peak altitude. The use of this approach ensures the production of explicitly doubly differentiable bottomside electron density profiles and directly avoids issues known to exist within current standards, such as the International Reference Ionosphere (IRI), which has discontinuities in space, time, and in the vertical electron density gradient. In terms of performance, after removing the impacts of hmF2 and NmF2, the new E‐CHAIM profile function generally performs comparably to the IRI, with bottomside TEC from both models within 2.0 TECU (1 TECU = 1016 e/m3) of observations. More specifically, the E‐CHAIM bottomside is demonstrated to outperform the IRI bottomside function in the F region during low solar activity periods with respect to incoherent scatter radar observations. At high latitudes, E‐CHAIM tends to outperform the IRI during winter months by between 10% and 40% of NmF2 while being outperformed by the IRI by between 10% and 25% of NmF2 during summer periods, mainly during the daytime at high solar activity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".