Accessing and Processing Real-Time GPS Corrections for Precise Point Positioning … Some User Considerations
Bibliographic record
Abstract
Natural Resources Canada makes its GPS Corrections (GPS·C) available in real-time through the Canada-wide Differential GPS Service. These corrections have also recently become available over the Internet using prototype UDP and TCP servers. The latter uses the NTRIP model of data transfer to allow, in part, for the creation of Virtual Reference Stations and the production of RTCM-SC104 local-area corrections for Differential GPS. In addition, both servers provide high-precision wide-area corrections in a modified RTCA-SC159 format to provide the necessary precision to eventually realize real-time Precise Point Positioning (PPP) to the subdecimetre level. Obtaining the highest possible accuracy from GPS corrections requires an awareness of the clock reference, or datum, to which they refer. The pseudorange measurements selected for the correction computation determine the clock reference for those corrections. The GPS·C corrections are currently referenced to the C/A (C1) and P2´ (C1+P2-P1) pseudorange observables and users must match these to achieve the best possible accuracy. Failure to do so can degrade positioning precision by up to a factor of three or more.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".