SA44B-07 - An updated assimilative CHAIM for near-real-time ionospheric specification:assessing its real-world performance
Bibliographic record
Abstract
The Assimilative Canadian High Arctic Ionospheric Model (A-CHAIM) is a near-real-time data assimilation model of ionospheric plasma density above 45 degrees North geomagnetic magnetic latitude. The model assimilates data from ground-based GNSS receivers, ionosondes, and the JASON altimeter satellites, using a particle filter technique. The model has recently been refined, building on two years of nearly continuous operation since 2019. In this study we will use the observation files gathered by the operational system to run the refined A-CHAIM data assimilation model and examine its performance during the May 12, 2021, Kp 7 geomagnetic storm. The modeling and forecasting ability of A-CHAIM will be evaluated by comparing the assimilation to in-situ measurements of topside electron density from the DMSP and Swarm satellites, as well as to manually-scaled ionograms from the Canadian High Arctic Ionospheric Network (CHAIN), ahead of updating the core A-CHAIM system with the refined model.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".