The Role of the Hydrographer in Rating Curve Development
Bibliographic record
Abstract
Stage-discharge rating curves are used to produce most of the world’s discharge data. The shape of these curves is dependent on the shape of the channel that controls flow. Changes in rating curves occur over time in response to transitory (e.g., vegetation, ice, debris) or persistent (e.g., aggradation/degradation) changes of the rated channel. Errors in rating curve development can result from the mischaracterization of the shape of the curve at a given time, or the misidentification of patterns of change over time. While data-driven methods for rating curve calibration are desirable, conventional statistical regression techniques, unfortunately, require far more data points to fully characterize the patterns of change in the curve shapes than are commonly available. This article discusses the benefits of field observations of the stream channel in support of rating curve development. The mathematical form of the rating curve can be inferred from observations of natural channel control features that link to principles of flow. In this article, the theoretical components of the rating curve equation are discussed with emphasis on how field observations can be used to groundtruth calibrated values for the coefficient, offset, and exponent for each segment of a stage-discharge rating curve. The article also explains how conceptual models developed by the hydrographer add value to the calibration process.
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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 | 0.026 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".