A Robust Estimation Method for Correcting Dynamic Draft Error in PPK GPS Elevation Using ADCP Tilt Data
Bibliographic record
Abstract
Abstract Measuring temporal and spatial variations in water level with high resolution and accuracy can provide fundamental insights into the hydrodynamics of marine and riverine systems. Real-time kinematic global positioning systems (RTK GPS), and by extension postprocessed kinematic (PPK) positioning, have provided the opportunity to achieve this goal, by allowing fast and straightforward measurements with subdecimeter accuracy. However, boat-mounted GPS are subject to movements of the water surface (e.g., waves, long-period heaves) as well as to the effects of dynamic draft. The latter contaminate the records and need to be separated and removed from the data. A method is proposed to postcorrect the elevation data using tilt information measured by an attitude sensor—in this case, an acoustic Doppler current profiler (ADCP) equipped with internal pitch and roll sensors. The technique uses iteratively reweighted least squares (IRLS) regressions to determine the position of the center of rotation (COR) of the boat that leads to optimal corrections. The COR is also allowed to change in time by performing the IRLS analyses on data subsamples, thus accounting for changes in weight distribution, for example, due to personnel movements. An example of application is presented using data collected in the Saint Lawrence fluvial estuary. The corrections exhibit significant reductions associated with the boat motion while keeping subtle variations in water levels likely related to local hydrodynamics.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".