Atmospheric gravity waves generated by solar wind high-speed stream Alfvén waves
Bibliographic record
Abstract
Solar wind Alfvén waves in high-speed streams from coronal holes modulate dayside ionospheric convection and currents, including auroral electrojets [1]. They generate large- to medium-scale atmospheric gravity waves (AGWs) propagating globally from sources in the lower thermosphere both upward and downward [2,3]. In the upper atmosphere, the AGWs drive traveling ionospheric disturbances (TIDs) observed by the Super Dual Auroral Radar Network (SuperDARN), Poker Flat Incoherent Scatter Radar (PFISR), and the GNSS total electron content (TEC) mapping technique. The horizontal equivalent ionospheric currents are estimated from the ground-based magnetometer data using an inversion technique. In the lower atmosphere, the equatorward propagating AGWs with attenuated amplitudes can be amplified upon over-reflection in the troposphere. They can release conditional symmetric instability leading to slantwise convection, latent heat release and intensification of extratropical cyclones [4,5], which in turn are a source of AGWs/TIDs. Southeastward propagating TIDs that originate from cold fronts of intensifying extratropical cyclones are observed in the detrended TEC maps, and by the multipoint and multifrequency continuous Doppler sounders in Czechia. Ray tracing AGWs in a model atmosphere supports the observations.[1] Prikryl P., et al., Ann. Geophys., 40, 619–639, 2022. doi.org/10.5194/angeo-40-619-2022[2] Mayr H.G., et al., Space Sci. Rev. 54, 297–375, 1990. doi:10.1007/BF00177800[3] Prikryl, P., et al., Ann. Geophys. 23, 401–417, 2005. doi.org/10.5194/angeo-23-401-2005[4] Prikryl P., et al., Ann. Geophys. 27, 31–57, 2009. doi:10.5194/angeo-27-31-2009[5] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 171, 94–10, 2018. doi:10.1016/j.jastp.2017.07.023
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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".