Effect of Grid Boundary Expansion to Include One Additional Data Source on Ionospheric Imaging Accuracy
Bibliographic record
Abstract
Four-dimensional ray tomography of ionospheric electron concentration using the Global Navigation Satellite System data is now a well-established technique. Since its advent, there have been a few studies of practical principles for optimizing crucial yet basic aspects of the problem for real experiments. For instance, optimal grid boundaries, voxel numbers, and voxel sizes must be determined case by case. This experiment examines the consequences of a small (mF2) parameter is compared with independent measurements from an Incoherent Scatter Radar (ISR) over a 12-month period, the expansion of the grid to include the extra data improves the accuracy of the imaging algorithm, regardless of which internal division definition is used. A convolution technique is used to obtain quantitative information about the differences between algorithm runs with different internal divisions and boundaries and the observed ISR values. Examination of the distribution of observed and reconstructed NmF2 values shows that the imaging algorithm does not produce as many low-valued results as the observed data.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".