Quantifying Uncertainty in Streamflow Records
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Uncertainty in hydrometric data is a fact of life. Basic assumptions about the nature of this uncertainty are necessary in every analysis of hydrometric data, and an understanding of the variability of uncertainty can facilitate the effective use of hydrologic information. For most of the twentieth century there has been little change in hydrometric methods and many analysts explicitly or implicitly assume that the uncertainty has not changed over the period of record. We argue that there is substantial variability in the magnitude of uncertainty in published streamflow records that is not transparent to data users. Quantifying uncertainty is particularly important in the context of the current changes in hydrometric technology and in the increasing integration of data sets from multiple providers. We recommend best practices for identifying uncertainty in field notes and propagating that observational uncertainty through the data production process. We suggest both field and reanalysis studies that could be undertaken to improve understanding of hydrometric uncertainty. We also recommend improvements in management practices, including preservation of relevant metadata and a suitable period of overlap for new and old observing systems to allow assessment of the effects of changing technology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it