Where is Positional Uncertainty
Bibliographic record
Abstract
Satellite positioning technology has improved so much in the last decade that absolute positioning accuracy anywhere on Earth at cm level accuracy is now readily available to users. Government and commercial wide area augmentation systems such as WAAS, OmniSTAR and Starfire promise decimetre accuracy to users in near real-time. Other webbased services such as AUSPOS, the Canadian NRCan service and OPUS provide an absolute coordinate in a few minutes to an accuracy of a few centimetres using the International GNSS Service (IGS) ground station network. Continuously Operating Reference Station (CORS) networks also provide a range of positioning services to users, predominantly in GDA. However the heritage of our national coordinate system, the Australian Geodetic Datum (AGD), was built on older techniques. The incredible precision of new satellite based positioning has revealed many distortions, even in the modern GDA94 datum. So how do we quantify the accuracy of coordinates? The Intergovernmental Committee on Surveying and Mapping (ICSM) have introduced a new term called Positional Uncertainty (PU) which refers to connection to datum (GDA94). Class will remain as a relevant measure and another new term, Local Uncertainty (LU), will replace Order. According to the ICSM Special Publication for Control Surveys (SP1), this change was to occur in 2005 (ICSM 2007). So how many states have implemented PU to their geodetic networks and made these numbers freely available to users? This paper will present an overview of PU and call for a national approach to implementing this important piece of meta-data across Australia. C. Roberts, S. Ozdemir, S. McElroy
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".