Determination of the Achievable Accuracy of Relative GPS/Geoid Levelling in Northern Canada 1
Bibliographic record
Abstract
It is well known that traditional spirit levelling, as a method for precise vertical positioning, suffers from a number of practical limitations caused by terrain roughness, harsh environmental conditions and restricted line-of-sight. In Canada, this is most evident when we look at the spatial distribution of vertical control stations, since the remote northern parts of the country are very poorly surveyed. A method that has been proven to be a useful and efficient alternative for vertical positioning in such environments is GPS-based levelling. A major advantage of GPS observations is that they are not affected (as much) by the practical limitations of spirit levelling. The achievable accuracy of this method, however, is still under question mainly because of datum inconsistencies and systematic errors inherent in the data. In this paper, a number of investigations are conducted to estimate the achievable accuracy of orthometric height determination in the northwestern parts of Canada, using GPS and geoid information in conjunction with various auxiliary parametric models (corrector surfaces) for describing datum offsets and systematic distortions. Specifically, the covariance (CV) matrix of the estimated parameters in the corrector surface model, and the combined relative accuracy of GPS and geoid data, are used to infer the accuracy of the orthometric height differences of newly established baselines in remote northern parts of Canada. A large test network consisting of the GPS benchmarks in western Canada is used for the computation of the covariance matrix of the estimated parameters in the corrector surface models, through a combined least-squares adjustment of GPS, levelling and geoid data. The results provide valuable insight into the role of the accuracy for the parameters in the corrector surface model for precise vertical positioning via GPS/geoid levelling. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".