Accuracy analysis of the GPS instrumental bias estimated from observations in middle and low latitudes
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Bibliographic record
Abstract
Abstract. With one bias estimation method, the latitude-related error distribution of instrumental biases estimated from the GPS observations in Chinese middle and low latitude region in 2004 is analyzed statistically. It is found that the error of GPS instrumental biases estimated under the assumption of a quiet ionosphere has an increasing tendency with the latitude decreasing. Besides the asymmetrical distribution of the plasmaspheric electron content, the obvious spatial gradient of the ionospheric total electron content (TEC) along the meridional line that related to the Equatorial Ionospheric Anomaly (EIA) is also considered to be responsible for this error increasing. The RMS of satellite instrumental biases estimated from mid-latitude GPS observations in 2004 is around 1 TECU (1 TECU = 1016/m2), and the RMS of the receiver's is around 2 TECU. Nevertheless, the RMS of satellite instrumental biases estimated from GPS observations near the EIA region is around 2 TECU, and the RMS of the receiver's is around 3–4 TECU. The results demonstrate that the accuracy of the instrumental bias estimated using ionospheric condition is related to the receiver's latitude with which ionosphere behaves a little differently. For the study of ionospheric morphology using the TEC derived from GPS data, in particular for the study of the weak ionospheric disturbance during some special geo-related natural hazards, such as the earthquake and severe meteorological disasters, the difference in the TEC accuracy over different latitude regions should be paid much attention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it