A revised method to extract thermospheric parameters from incoherent scatter observations
Bibliographic record
Abstract
Height distribution of ionospheric plasma parameters in the F2-region is closely related to height distribution of the main thermospheric parameters. Therefore, they can be extracted from ionospheric observations solving an inverse problem of aeronomy. A self-consistent approach to the Ne(h) modeling at the F2-region heights has been applied to solve the problem. Using routine incoherent scatter radar observations (Ne(h), Te(h), Ti(h), Vi(h) profiles) the method yields a self-consistent set of main aeronomic parameters responsible for the F2-region formation. The list of derived parameters includes: neutral temperature profile Tn(h) depending on the exospheric temperature Tex, the temperature at 120 km T120 and the shape-parameter S, which determine the temperature profile, concentration of neutral species [O], [O2], [N2], vertical plasma drift W, which may be converted to the meridional thermospheric wind Vnx, total solar EUV flux and ion composition (O+, O2 +, NO+, N2 +, N+) as a result of Ne(h) fitting. Therefore, the method gives a complete description of the upper atmosphere condition in the vicinity of incoherent scatter facility for the periods of observation. Analysis of all available EISCAT (CP-1, CP-2) observations has shown wide deviations from MSIS-86 model predictions for geomagnetically disturbed conditions while the retrieved parameters are close to the model ones for quiet periods. The approach turns out to be very useful for physical analyses of the F2-layer disturbance mechanisms giving a complete picture of the phenomenon in question. Limitations and problems related to method application are discussed. Under existing conditions when thermospheric observations are not conducted currently the proposed method may be considered a real tool for thermosphere investigation and monitoring at least for the periods of ISR observations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".