Reproducibility and uncertainty for national Canadian hydrometric stations
Bibliographic record
Abstract
Reliable and accurate river streamflow or discharge measurement and reporting are essential for engineering, economic, and social decision-making. Discharge values are often perceived as true and deterministic by users, modelers, and decision-makers. In this study, the processes of discharge estimation by the Water Survey of Canada, WSC, are presented. The process of inferring the discharge (water volume over time) based on stage (water level) through stage-discharge relationships or “rating curves” including related terminologies is described. Multiple practices of rating curve construction and discharge estimation across WSC hydrometric stations are explored. Major processes of "override" and "temporary shift" which significantly affect the discharge estimation are elaborated. The reproducibility of the published discharge data using data from the production process for approximately 1750 active hydrometric stations operated by WSC is examined. Other impacts of temporary shift and override have been evaluated on the properties such as discharge residuals or performance metrics. Recommendations are made for wider access to metadata and measurements that are essential to quantify the reproducibility and uncertainty of reported discharge values. Open science, particularly Earth system modeling, demands clear communication of reproducibility, and uncertainty of published discharge.
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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 | 0.043 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".