Comment on “The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records” by de Oliveira and Vrugt
Bibliographic record
Abstract
Abstract Quantifying uncertainties and errors in hydrometric observations is critical to improve our ability to predict streamflow. de Oliveira and Vrugt (2022, https://doi.org/10.1029/2022wr032263 ) expand on an earlier publication (Vrugt et al., 2005, https://doi.org/10.1029/2004wr003059 ) to describe a difference‐based variance estimation method that is, used to estimate the aleatory observation uncertainty in streamflow records directly from a time series of such data. The method is then applied to hourly data from 500+ catchments across the contiguous United States. This comment outlines our concerns that the assumptions needed to effectively use difference‐based variance estimation methods are not always met by hourly streamflow records. We illustrate our concerns through the use of a specific test case. We argue that more work is needed to quantify the individual sources of errors and uncertainties in hydrometric observations.
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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 | 0.018 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.038 | 0.045 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".