Ratingcurve: A Python Package for Fitting Streamflow Rating Curves
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
Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure proxy like water surface elevation (stage). Despite the existence of automated methods, most rating curves are still fit manually, which can be time-consuming and subjective. Although several automated methods exist, they vary greatly in performance because of the non-convex nature of the problem. In this work, we develop a parameterization of the segmented power law that works reliably with minimal data, which could serve operationally or as a benchmark for evaluating other methods. The model, along with test data and tutorials, is available as an open-source Python package called ratingcurve. The implementation uses a modern probabilistic machine-learning framework, which is relatively easy to modify so that others can improve upon it.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it