Probabilistic Prediction and Forecast of Daily Suspended Sediment Concentration on the Upper Yangtze River
Bibliographic record
Abstract
Abstract Sediment transport in suspension can represent more than 90% of a river's total annual flux of sediment. In the case of the Yangtze River, more than 99% of the sediment supplied to the sea is suspended load. Suspended sediment is thus an important component of the total sediment load, with implications for channel dynamics, landscape evolution, ecology, and human‐related activities. For hydrological management of large basins such as the Yangtze River, knowledge of the processes governing suspended sediment concentration (SSC) is essential. An analysis of the temporal variation of SSC for the Upper Yangtze basin (defined at Pingshan station) is presented here. For this purpose, a database of 50 years of concurrent discharge and SSC measurements, made by the Yangtze River Commission, is used. The analysis is made using a novel probabilistic data‐driven technique, the Generalized Pareto Uncertainty (GPU). This technique allows for the testing of several strategies of prediction and forecast applied to a time series of SSC and streamflow. Changing between local or seasonal variables to feed these strategies, we inferred that although the main driver of the SSC transport is flow (as reported by previous authors), sediment storage is also a major control. Furthermore, the maximum necessary time lag for forecasts made with the data is on the order of one week, which provides one indication of the time scale of the local processes of SSC transport in the Upper Yangtze. In this paper, limitations and data requirements of the GPU methodology are also discussed.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".