Acoustic Doppler current profiler discharge measurement data used for QUant multiple-transect uncertainty analysis
Bibliographic record
Abstract
Acoustic Doppler current profiler (ADCP) discharge measurement data were collected and analyzed for use in developing an operational uncertainty analysis tool known as QUant (Moore and others, 2016). These ADCP measurements were originally collected in the United States, Canada, and New Zealand as a part of research conducted to validate ADCP discharge measurements made with Teledyne RD Instruments RiverRay and SonTek M9 ADCPs (Boldt and Oberg, 2015). The data were chosen in order to represent a variety of geographic and streamflow conditions, such as mean depth and mean velocity. Due to current limitations in the QUant software, only measurements collected using Teledyne RD Instruments Rio Grande and StreamPro ADCPs were used. All measurements were collected and processed with WinRiver II (Teledyne RD Instruments, 2016). An appropriate method for estimation of flow near the water surface and the streambed was obtained by means of the extrap software (Mueller, 2013). The extrapolation method and parameters obtained with extrap were entered into WinRiver II and reprocessed before use in QUant. Due to the complexity of an ADCP data file and the various algorithms applied to compute the streamflow from ADCP data, these data are most useful in their original raw data format which can be opened and processed in either WinRiver II, which is available without cost at: http://www.teledynemarine.com/rdi/support#. Each measurement consists of: *.mmt file; an xml configuration file used by WinRiver II for instrument setup, specific measurement data entry, and filenames of the raw transect data files (*.pd0). *.pd0 files; the raw binary data collected by WinRiver II. The format for these files is defined in Teledyne RD Instruments (2016). *.txt files; raw ASCII data from external sensors such as GPS receivers. These data are not used in WinRiver II nor for the present analyses. *_extrap.txt file; a file that summarizes the method and parameters selected for estimation of near-surface and near-bed discharges. WinRiver.pdf files; a file that provides a summary of the discharge measurement in pdf format. References Boldt, J. A., and Oberg, K. A., 2016, Validation of streamflow measurements made with M9 and RiverRay Acoustic Doppler current profilers: Journal of Hydraulic Engineering, v. 142, no. 2. [Also available at https://doi.org/10.1061/(asce)hy.1943-7900.0001087.] Moore, S. A., Jamieson, E. C., Rainville, F., Rennie, C. D., and Mueller, D. S., 2017, Monte Carlo approach for uncertainty analysis of Acoustic Doppler current profiler discharge measurement by moving boat: Journal of Hydraulic Engineering: v. 143 no. 3. [Also available at https://doi.org/10.1061/(asce)hy.1943-7900.0001249.] Mueller, D. S., 2013, extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements: Computers & Geosciences, v. 54, p. 211-218. [Also available at https://doi.org/10.1016/j.cageo.2013.02.001.] Teledyne RD Instruments, Inc., 2016, WinRiver II Software User's Guide, P/N 957-6231-00, San Diego, CA, 310 p.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".