Bibliographic record
Abstract
Differential Code Biases (DCBs) are much more relevant when GNSS data processing with code measurements is involved, such as in ionospheric sensing, positioning, and timing. The current approach to estimate DCBs is based on carrier-phase smoothed code observations together with ionospheric modeling. A limiting factor of the method is the effect of the leveling errors from the smoothing process on the DCB estimate. To reduce the leveling errors, a new DCB estimation method based on an Uncombined Precise Point Positioning (UPPP) model is proposed. A month's data from a global network in a high solar activity year from May 1 to 31, 2014 are processed to validate the method. The results show that most satellite DCB estimates are found to be more stable than when using the smoothed code method. The improvement can be up to about 0.22 ns. The stability and accuracy of the receiver DCB estimates is also enhanced. Copyright © 2017 Institute of Navigation
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".