Gauging Rivers during All Seasons Using the Q2D Velocity Index Method
Bibliographic record
Abstract
This paper presents a new model (Q2D) for the velocity distribution in a channel cross section for use in estimating discharge. It describes the model and its theoretical basis and presents the results of a case study. The distribution is determined by combining the principle of maximum entropy with a probability distribution obtained by the solution of the Poisson equation over the cross section. The model uses observed depth and velocity in the water column, where an acoustic Doppler current profiler is installed to determine three key flow parameters to obtain velocity and discharge. In addition, if supporting field discharge measurements are available, the model can be further calibrated to account for any asymmetry in the flow. If velocity distribution data exist for the entire cross section, the model can be adjusted to stretch the predicted velocity pattern to better conform to experimental observations. When applied to the Châteauguay River, Quebec, for both ice covered and open water, Q2D predicted 12 gauged discharges with a −4% bias and an average absolute error of 7% prior to calibration. After removing the bias through calibration, the average absolute error was reduced to 5%.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".