Analysis of the Capacity of a Double Rating Curve to Reproduce the Flows Measured by Doppler Fixed at the Bottom of an Ice Covered River
Bibliographic record
Abstract
The classic approach for estimating river flows is based on the use of a rating curve, which links the flow rates to the water heights measured at a gauging station. This simple approach, has practical and economic advantages but also has limitations related in part to the difficulty of obtaining a representative range of flows, particularly in the presence of ice cover. The objective of this study is to show how it is possible to improve the results obtained by a traditional rating curve using the validated measurements of the Acoustic Doppler Velocity Meter (ADVM) of the Argonaut SW (shallow water) Doppler obtained in continuous, in the presence of ice sheet. The proposed methodology consists in filtering and validating the height measurements provided by the standard gauge as well as the speed and height the measurements provided by the Doppler SW. The validated measurements are then split into two parts: The first part is used for the calibration of a double rating curve that links the series of validated levels obtained by the standard limnimeter, to the flow obtained from the Doppler SW. The second part is used to evaluate the capacity of the double rating curve obtained during the calibration, and to estimate the flow measured by the Doppler SW. The double rating curve thus calibrated reproduces the flow measured by the Doppler SW with an average deviation of 5.03%. In the presence of ice cover, this average difference is 7.68%. These results show the interest of a combined use of the Doppler SW and the double rating curve, for monitoring river flows under ice cover. The Doppler SW makes data available, necessary to calibrate a reliable rating curve, based on a wide range of flow variations covering the winter period.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".