Bibliographic record
Abstract
The accuracy and capacity to resolve meso‐scale structures of a four dimensional ionospheric imaging algorithm in the circumstance of data from dense networks of permanent GNSS ground receiver stations were investigated. Simulation studies were conducted in order to be able to assess the performance of the algorithm over the entire imaged region. The Multi‐instrument Data Assimilation Software (MIDAS) algorithm was used for this purpose. Simulated input data in Receiver Independent Exchange Format (RINEX) were produced by calculating slant Total Electron Content (sTEC) values for satellite to receiver raypaths through an artificially generated ionosphere. Modeling these signals including Differential Code Biases (DCBs) and noise had negligible impact on the output from the imaging algorithm when compared with modeled signals that included neither. Comparing the output from MIDAS using a range of grid definitions show that finer grids have improved capacity to resolve meso‐scale structures in the input model but over all are less accurate than coarser grids. The greatest errors occur in low‐data regions of the grid and where structures in the input have the greatest gradients in vertical Total Electron Content (vTEC). A good compromise between the conflicting needs of resolution and accuracy is given by a grid defined with 2° × 2° latitude by longitude local horizontal grid divisions.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".