Toward capturing the behaviour of polar cap absorption events in empirical models: A fast, quasi-empirical solar energetic proton module
Bibliographic record
Abstract
Solar Energetic Protons (SEPs) strongly modulate the ionization of the high latitude D-Region, often producing severe Polar Cap Absorption (PCA) during large SEP events that can disrupt High Frequency (HF) propagation signal integrity for several days at a time. Many operational models of PCA exist and do a decent job at capturing the overall absorption resulting from these events; however, these models are often either computationally intensive physics-based models, which attempt to capture the response of the chemically complex D-Region ionization, or are 2D models based on empirical measurements from relative ionospheric opacity meters (riometers). Here we propose a simpler SEP module that is both computationally efficient and performs at a similar, or even better, level to the existing standard. The module itself is based in part on the work of Rogers et al. [2016] with extension to full 3D specification and improvements in flexibility. This presentation will provide an overview of this module, while also examining its performance and demonstrating a pathway through which it could be either integrated directly into the International Reference Ionosphere (IRI) or provided as a supplementary tool for IRI users concerned with high latitude HF absorption. An example of the output from this module during the September 2017 SEP event can be found here: https://youtu.be/pSbk1kVY7pc References Rogers, N. C., A. Kero, F. Honary, P. T. Verronen, E. M. Warrington, and D. W. Danskin (2016), Improving the twilight model for polar cap absorption nowcasts, Space Weather, 14, 950–972, doi:10.1002/2016SW001527.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".