Enhancements and validation of the real-time optimised D-Region HF radio absorption model
Bibliographic record
Abstract
The Optimised D-Region Absorption Model (ODRAM) provides global nowcasts and forecasts of ionospheric HF radio wave absorption. This results from ionisation by solar flares, Solar Energetic Particles (SEP), and auroral electron precipitation. Parameters of ODRAM are optimised in near real time by assimilating satellite measurements, geomagnetic index estimates, and direct measurements of absorption made by riometers at high latitudes. In this presentation, we validate two new empirical models for the solar flare (shortwave fadeout) model developed from riometer measurements at 22 locations in Canada and Finland recorded during 126 X-class flares in Solar Cycle 23. We then discuss improvements to the empirical Auroral Absorption (AA) models obtained by removing artefacts in the entire measurement archive and an assessment is made of the optimal AA model driver parameters (selected from real-time geomagnetic index estimates or proxies derived from in situ solar wind/IMF measurements). Finally, we assess the combined model performance for a period of active space weather in September 2017. Issues relating to riometer placement, measurement calibration, and artefact removal will be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".