Limitations in Extraction of Survey Data from Real-Time Kinematic GPS ADCP Systems
Bibliographic record
Abstract
A total of 595 real-time kinematic global positioning system (RTK-GPS) water surface elevations (WSE) were measured using georectified and nongeorectified survey systems along a 21-km-long river reach to evaluate vertical coordinate accuracy of a nongeorectified RTK-GPS acoustic Doppler current profiler (ADCP) system. A georectified survey of a series of quality control benchmarks produced a vertical root-mean-square error (RMSE) of 0.033 m, which was considered globally accurate for river survey purposes. The WSE measured simultaneously at the same locations comparing the nongeorectified to the georectified system resulted in a RMSE=0.741 m. Evaluation of intersetup precision of the nongeorectified WSE confirmed relative accuracy to each initialized arbitrary vertical datum but were found not to be globally accurate. Only 7% of the nongeorectified base station setups resulted in RMSE less than the combined manufacturer’s tolerances when compared to the georectified system at the same spatial locations. When vertical datum corrections were applied to the nongeorectified system from the globally accurate RTK-DGPS system, 80% of the base station setups demonstrated RMSE within the combined manufacturer’s tolerances. Datum corrections varied greatly, ranging from −2.43 to +1.80 m. This study demonstrates that when using nongeorectified RTK-GPS ADCP systems for purposes other than discharge measurement, such as in the extraction of bathymetry data for use in hydraulic assessments and models, great care must be exercised when verifying WSEs
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".