Evaluation of ADCP Apparent Bed Load Velocity in a Large Sand-Bed River: Moving versus Stationary Boat Conditions
Bibliographic record
Abstract
Detailed mapping of bathymetry and apparent bed load velocity using a boat-mounted acoustic Doppler current profiler (ADCP) was carried out along a 388-m section of the lower Missouri River near Columbia, Missouri. Sampling transects (moving boat) were completed at 5- and 20-m spacing along the study section. Stationary (fixed-boat) measurements were made by maintaining constant boat position over a target point where the position of the boat did not deviate more than 3 m in any direction. For each transect and stationary measurement, apparent bed load velocity (vb) was estimated using ADCP bottom tracking data and high precision real-time kinematic (RTK) global positioning system (GPS). The principal objectives of this research are to (1) determine whether boat motion introduces a bias in apparent bed load velocity measurements; and (2) evaluate the reliability of ADCP bed velocity measurements for a range of sediment transport environments. Results indicate that both high transport (v¯b>0.6 m/s) and moving-boat conditions (for both high and low transport environments) increase the relative variability in estimates of mean bed velocity. Despite this, the spatially dense single-transect measurements were capable of producing detailed bed velocity maps that correspond closely with the expected pattern of sediment transport over large dunes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".