A study of smoothed TEC precision inferred from GPS measurements
Bibliographic record
Abstract
Abstract The availability of a large amount of TEC data derived from dual frequency GPS measurements observed by GPS reference networks provides a great opportunity for ionosphere studies. In order to obtain better accuracy for the derived TEC, a data smoothing technique is usually employed to take advantage of both code pseudorange and carrier phase GPS measurements. The precision of TEC data therefore is dependent on the smoothing approach. However little work has been done to evaluate the precision of the smoothed TEC data obtained from different smoothing approaches. This investigation examines the properties of two popularly used smoothing approaches and develops the closed-form formulas for estimating the precision of the smoothed TEC data. In addition, a previously proposed approximate formula for estimating TEC precision is also evaluated against its closed-form formula developed in this paper. The TEC precisions derived from the closed-form precision estimation formulas for approaches I and II are analyzed in a numerical test. The results suggest that approach II outperforms approach I and the precision of TEC data smoothed by approach II is higher than approach I. For approach I, a numerical test is also conducted to compare the precision difference between the closed-form and approximate formulas for estimating TEC precision. The comparison indicates that TEC derived from the closed-form formula have better precisions than the approximate formula. Analysis also reminds users that extra cautions should be taken when using the approximate formula in order to avoid the precision divergence phenomenon.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".