Turbulent times in the northern polar ionosphere?
Bibliographic record
Abstract
A model is presented of the growth rate of turbulently generated irregularities in the electron concentration of northern polar cap plasma patches. The turbulence is generated by the short‐term fluctuations in the electric field imposed on the polar cap ionosphere by electric field mapping from the magnetosphere. The model uses an ionospheric imaging algorithm to specify the state of the ionosphere throughout. The growth rates are used to estimate mean amplitudes for the irregularities, and these mean amplitudes are compared with observations of the scintillation indices S4 and σϕ by calculating the linear correlation coefficients between them. The scintillation data are recorded by GPS L1 band receivers stationed at high northern latitudes. A total of 13 days are analyzed, covering four separate magnetic storm periods. These results are compared with those from a similar model of the gradient drift instability (GDI) growth rate. Overall, the results show better correlation between the GDI process and the scintillation indices than for the turbulence process and the scintillation indices. Two storms, however, show approximately equally good correlations for both processes, indicating that there might be times when the turbulence process of irregularity formation on plasma patches may be the controlling one.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".