Revisiting the Behavior of the <i>E</i>‐Region Electron Temperature During Strong Electric Field Events at High Latitudes
Bibliographic record
Abstract
Abstract A rich data set acquired during a long‐lived strong electric field event by the north‐facing Resolute Bay incoherent scatter radar confirms and strengthens conclusions previously drawn from several less comprehensive studies of E ‐region electron heating by large amplitude Farley‐Buneman waves. For the exceptionally abundant set of very good quality data we uncovered, the E ‐region electron temperature response to strong ambient electric fields is described very accurately by a simple linear function of the electric field at 110 and 117 km altitudes. The linear dependence starts at 40 mV/m and shows no hint of deviating from the linear response up to 150 mV/m (the maximum electric field observed during this event). Based on this new evidence, we have revisited previous E ‐region electron temperature observations from various altitudes and have built a model that is consistent with present and past observations. The model is made of simple linear variations in the electron temperature with slopes that depend on altitude. It should prove to be a useful reference for anyone interested in the E ‐region electron temperature anywhere between 100 and 120 km altitudes.
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.
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 | 0.000 |
| 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 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".