An analysis of single point positioning with real-time internet-based precise GPS data
Bibliographic record
Abstract
Single Point Positioning (SPP) is currently capable of providing position accuracy of several meters. To obtain a better accuracy, the Differential GPS (DGPS) method must be applied. For large-scale applications such as aerial survey and mapping, however, the requirement of a base station(s) in conventional DGPS often become problematic in practice due to the increased operational cost and complexity. Recently a concept of Global Differential GPS (GDGPS) has attracted increasing interests among the GPS communities. GDGPS has the same user’s implementation as SPP, but its accuracy is augmented by the globally or regionally distributed precise GPS data currently including precise satellite orbit and clock corrections. The major advantage of GDGPS lies in two aspects: system simplicity at the user’s end, and globally consistent positioning accuracy. This paper presents GDGPS positioning results using the precise GPS data generated by the Natural Resources Canada (NRCan). NRCan’s precise data can be retrieved real-time from Internet base on Virtual Private Network (VPN) and Multicast technology. The packet delay and Packet Loss Rate (PLR) of multicasting over Internet will first be investigated. The total latency of precise GPS data as well as the position accuracy of GDGPS will then be analyzed. The numerical results have shown that a meter to half-meter level accuracy is obtainable based on epoch-by-epoch data processing. With phase-smoothed code observations, the positioning accuracy can be further improved.
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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 | 0.000 | 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 | 0.001 |
| 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".