Numerical forecast of the upper atmosphere and ionosphere using GAIA
Bibliographic record
Abstract
Abstract Upper atmospheric conditions are crucial for the safe operation of spacecraft orbiting near Earth and for communication and positioning systems using radio signals. To understand and predict the upper atmospheric conditions, which include complex variations affected by both low altitude and upper surrounding environments, we have developed a quasi-real-time and forecast simulations using a physical global model, the Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy (GAIA). The GAIA simulation system provides a global distribution of ionospheric total electron content (TEC) with background atmospheric and electric distributions including a few-days prediction. The prediction accuracy for the detection of significant ionospheric storms decreases with increasing lead time, i.e., the duration of the model simulation which is not constrained by realistic input parameters. Similar characteristic variations associated with sudden stratospheric warmings (SSWs) are reproduced with the full or limited input of meteorological data at least the prior 3 days. This is a first step toward the usage of GAIA for space weather forecasting.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".