HF detection of slow long‐lived <i>E</i> region plasma structures
Bibliographic record
Abstract
During the equinox and winter seasons, and in the range 300–1000 km the Saskatoon Super Dual Auroral Radar Network (SuperDARN) radar often detects extended patches of coherent echoes with remarkably uniform properties and low Doppler speeds, in the range 0 to 200 m/s. Typically, these echoes last for ∼3 hours, and are observed between 1300 and 2300 MLT, at times of moderate to high Kp values. The echo Doppler shift changes systematically with azimuthal angle and a vector reconstruction of the implied drift indicates westward velocities in the range 150 to 250 m/s, well below the threshold speed associated with Farley‐Buneman waves. When ionosonde observations are available, they invariably show the presence of a thick sporadic E layer. This feature, plus the facts that the IMF By is always negative and that the echoes are equatorward of the regions of discrete precipitation (as indicated by comparison with coincident DMSP satellite observations), indicate that the echoes are associated with the diffuse aurora in regions where the electric field is of the order of 10 mV/m or less. We infer from these echo properties that the irregularities are triggered by a primary gradient‐drift mechanism which then cascades to the observed structures through weakly turbulent mode‐coupling processes. Several events were observed during special multifrequency experiments using the Saskatoon SuperDARN radar. It was found that the Doppler speed, power, and spectral width all increase systematically with increasing radar frequency. The findings for Doppler speed and power appear to arise, at least in part, from the increase in height of the radar echoes with increasing frequency. The frequency dependence of spectral width may be related to instability lifetimes; it was found to agree well with the results of numerical simulations [Keskinen et al., 1979].
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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 | 0.000 | 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) | 0.001 | 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".