Generating electron density archives using mainland EISCAT data between 2001 and 2021 at 10 min and 1 h integration
Bibliographic record
Abstract
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, Norway (UHF and VHF) have been measuring ionospheric parameters, such as electron density, since 1985 making it an excellent resource to study changes in the ionosphere over a long time period. This paper details how we have combined high elevation data from both radars between 2001 and 2021, re-integrated at 10 min and 1 h, to look at the different sources of variability in the MLTI region between 50 and 200 km. Day of year climatology’s of the electron density highlight that the VHF data are more prone to contamination from Polar Mesospheric summer Echos. The magnetic local time variation of the electron density shows seasonal and altitude dependence related to solar UV illumination and electron precipitation, as expected. We compare our archives to the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) and find the biggest differences during the winter months and below 100 km, where the model does not yet include the impact of high energy electron precipitation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".