Precise Cooperative Positioning: A Case Study in Canada
Bibliographic record
Abstract
he convergence period associated with precise point positioning (PPP) is often a limiting factor for the adoption of this technique in applications requiring short on-site occupation times. Centimeter-level accuracies can be obtained much faster from PPP when external information on the ionosphere is provided from nearby reference stations. This strategy is however not well suited for countries with a sparse distribution of permanent reference stations, such as Canada. To overcome this limitation, a densification of the network is first obtained by consolidating all GNSS data from government agencies, universities and provincial networks. The location of PPP users in Canada also suggests that a cooperative approach, where users exchange ionospheric information, is feasible. Based on a week of data submitted to Natural Resources Canada’s online PPP service, it is shown that a cooperative approach, using both permanent stations and users as reference stations, can potentially provide better than 10-cm horizontal accuracies with a single epoch of data for nearly 68% of users.
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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 | 0.001 |
| 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".