Narrow width Farley-Buneman spectra under strong electric field conditions
Bibliographic record
Abstract
As a rule, the phase velocity of unstable Farley-Buneman waves is found not to exceed the ion-acoustic speed, cs. However, there are known exceptions: under strong electric field conditions, much faster Doppler shifts than expected c s values are sometimes observed with coherent radars at high latitudes. These Doppler shifts are associated with narrow spectral width situations. To find out how much faster than c s these Doppler shifts might be, we developed a proper c s model as a function of altitude and electric field strength based on ion frictional heating and on a recently developed empirical model of the electron temperature under strong electric field conditions. Motivated by the ‘narrow fast’ observations, we then explored how ion drifts in the upper part of the unstable region could add to the Doppler shift observed with coherent radars. While there can be no ion drift contribution for the most unstable modes, and therefore no difference with c s for such modes, under strong electric field conditions, a large ion drift contribution of either sign needs to be added to the Doppler shift of more weakly unstable modes, turning them into ‘fast-‘ or ‘slow-’ narrow spectra. Particularly between 110 and 115 km, the ion drift can alter the Doppler shift of the more weakly unstable modes by several 100 m/s, to the point that their largest phase velocities could approach the ambient E x B drift itself.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".