Uncertainty analysis of stage-discharge rating curves for seven rivers at Calvert Island (2013-2015)
Bibliographic record
Abstract
Technical report on the development of stage-discharge rating curves for seven rivers draining into Kwakshua Channel (Calvert Island, BC, Canada) and a methodology on discharge uncertainty quantification. The aim of this study was to develop rating curves for seven watersheds in the bog forest environment of Calvert Island, British Columbia, Canada and to investigate the uncertainties related to rating curve development and discharge estimation using the velocity-area and salt dilution discharge measurement methods. Rating curves with 99% confidence intervals were created with good curve fitting results (R2 > 0.97) for three out of seven watersheds. Discharge measured with the velocity-area method had a higher average uncertainty (13.5 – 15.6%) compared to the salt dilution method (5.1%). The largest source of error for the velocity-area method was the uncertainty in the velocity readings followed by the uncertainties in the depth readings, the calibration and systemic errors and uncertainties in the width measurements. Increasing the number of stations gives a better representation of river cross-section and velocity profile. The most important source of error for the salt dilution method was improper salt mixing. The second most important source of error was the uncertainty associated to the calibration correction factor. Hysteresis was not considered a source of uncertainty, but the extrapolation of a rating curve beyond a range of low flow discharge measurements resulted in an underestimation of discharge at higher stages. Lineage a242acd4-e3c7-46e0-8f43-f428fb824018 1347af6c-aedf-4ec6-bd37-ed508df6c40a
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".