Assessing Detectability of Change in Low Flows in Future Climates from Stage Discharge Measurements
Bibliographic record
Abstract
This study assesses whether statistical changes in low flows could be detected should they occur in future climates. Since rating curves are a key stage in the development of a discharge record, their statistical attributes determine if changes in flows can be detected. However, since uncertainty about a rating curve is heteroscedastic (i.e., the errors are drawn from different distributions for different values of the independent variables) there is a need to use a statistical procedure that correctly allocates uncertainty. A simple statistical procedure that allows a stepwise estimate of the variance of the rating curve is used in this paper. The procedure estimates the variance components over finite intervals of a generalized function and allows isolation of seasonal measurements, in particular measurements made during winter conditions. The procedure is demonstrated for one rating curve and the method is used to determine confidence limits for low flows for both summer and winter measurements from 17 stations in south-central British Columbia. The uncertainty for low flows in the warm temperature seasons of summer and fall is low compared to the uncertainty for low flows during the cold temperatures of winter. This indicates that small changes in summer low flows will be detectable, while similar changes in winter low flows cannot be resolved.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".