Poker Flat Incoherent Scatter Radar observations of anomalous electron heating in the E region
Bibliographic record
Abstract
Abstract. A comprehensive 2-year dataset collected with the Poker Flat Incoherent Scatter Radar (PFISR) located near Fairbanks, Alaska (MLAT = 65.4° N) is employed to identify and analyse 22 events of anomalous electron heating (AEH) in the auroral E region. The overall AEH occurrence probability is conservatively estimated to be 0.3% from nearly-continuous observations of the E region by PFISR, although it increases to 0.7–0.9% in the dawn and dusk sectors where all AEH events were observed. The AEH occurrence variation with MLT is broadly consistent with those of events with high convection velocity (>1000 m s−1) or electron temperature (> 800 K), except for much smaller AEH probability and absence of AEH events near magnetic midnight. This suggests that high convection electric field by itself is necessary but not sufficient for measurable electron heating by two-stream plasma waves. The multi-point observations are utilised to investigate the fundamental dependence of the electron temperature on the convection electric field, focusing on the previously-proposed saturation effects at extreme electric fields. The AEH dataset was found to exhibit considerable scatter and, on average, similar rate of the electron temperature increase with the electric field up to 100 mV m−1 as compared with previous studies. At higher (highest) electric fields, the electron temperatures are below the linear trend on average (within uncertainty). By employing a simple fluid model of AEH, it is demonstrated that some of this deviation from the linear trend may be due to a stronger vibrational cooling at very large temperatures and electric fields.
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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 | 0.000 |
| 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) | 0.000 | 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".