Investigating seasonal to decadal variability in the electron density of the mesosphere using historical EISCAT data
Bibliographic record
Abstract
The mesosphere/lower-thermosphere/ionosphere (MLTI) region is a critical boundary in the coupling of the atmosphere, climate and space weather, however it is one of the least understood regions, making it hard to include in whole atmosphere models. The EISCAT radars at Tromsø (UHF and VHF) have been measuring ionospheric parameters, such as electron density, for almost 4 decades making them an excellent resource to study changes in the ionosphere over a long time period. We have generated two data archives using 20 years of observations of EISCAT Tromsø from 2001 to 2021; the data have been re-analysed at 10-minutes and 1-hour integrations. These archives are used to study the different sources of variability in the MLTI from 50-200 km. This is the first time the mainland EISCAT data has been converted into a format that allows for long term statistical study. We have created electron density climatologies split by solar, geomagnetic and atmospheric indices to investigate the different drivers of variability in the MLTI region. We show seasonal averages of the electron density altitude profiles and compare our results to the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) and the Whole Atmosphere Community Climate 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".