Bibliographic record
Abstract
Abstract. Accurate discharge values play a critical role in water resource planning and management. However, it is common for users, modelers, and decision-makers to consider these values as true and deterministic, despite the subjective and uncertain nature of the estimation process. To address the issue, this study was conducted to identify the discharge estimation methods and associated uncertainties of hydrometric measurements in Canada. The study involved an exploration of multiple operating procedures for rating curve construction and discharge estimation across 1800 active Water Survey of Canada (WSC) hydrometric stations using an independent workflow. The first step involved understanding the discharge estimation process used by the WSC and the standard operating procedures (SOP) for inferring discharge from stage measurements. During the implementation of the workflow, it was observed that manual intervention and interpretation by hydrographers were required for time-series sequences labeled as "override" and/or "temporary shift". The workflow demonstrated that 67 % of existing records could be adequately recreated following the rating curve and temporary shift concept, while 33 % followed the other discharge estimation methods (override). Novel methods for discharge uncertainty estimation should be sought given the practices of override and temporary shift by the WSC. This study attempts to reconcile the significant issue of estimating uncertainty in published discharge values, particularly in the context of open science and Earth System modeling. By collaborating with the WSC, this research aims to improve the understanding of the processes used for discharge estimation and promote wider access to metadata and measurements for more accurate uncertainty quantification.
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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.196 | 0.111 |
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".