Bibliographic record
Abstract
Single baseline Real-time Kinematic (RTK) carrier phase GPS positioning is limited the inability to resolve carrier phase ambiguities in the presence of large baseline length dependent errors. During periods of high ionospheric influence, the maximum distance from the reference station allowing high-accuracy RTK positioning might be as low as 10 km, or less. Network RTK methods ideally provide positioning with errors independent of the rover position in the network. Several methods to implement network RTK have been proposed and tested in recent years. A current network RTK messages proposal, known as the Master-Auxiliary concept and based on betweenstation single difference corrections, is discussed in the RTCM SC104 work group as part of the RTCM 3.0 standard. In this scheme, the network software will focus on resolving network ambiguities and is relieved of the duties of interpolating the corrections and fitting a model or creating a virtual reference station. In this method, the rover receiver has access to all the network information. It becomes the rover receiver’s or baseline processing software’s responsibility to interpret and apply the network corrections. The receiver in the RTCM 3.0 network scheme is much more informative and has a greater allowance for upgrading. The rover is a true network receiver, not a ‘deceived’ one, as in the virtual reference station (VRS) method. This paper discusses the RTCM 3.0 implementation in PLAN group network software MultiRef TM and addresses novel features of the new standard. A post-mission test has been performed with field data from the South Alberta Network (SAN). Three interpolation techniques are discussed in the latter part of this paper: distance weighted, plane and collocation. The dispersive and non-dispersive errors after correction using these three interpolation methods are compared with the errors without correction. Last, a comparison in position domain is given. All results show that RTCM 3.0 is a preferable implementation for network RTK by reducing the sizes of the network RTK corrections, as well as the transmission of satelliteindependent error information. All three interpolation techniques are feasible and obtain similar results in position domain in this test. Further analysis and tests concerning these three interpolation methods will be performed in the future.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".