Generating electron density archives using mainland EISCAT data between 2001 and 2021 at 10 min and 1 h integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it