Intraseasonal‐to‐Interannual Analysis of Discharge and Suspended Sediment Concentration Time‐Series of the Upper Changjiang (Yangtze River)
Bibliographic record
Abstract
Abstract Sediment transport is the main driver of the channel morphology and landscape evolution, with implications for chemical and biological river processes, and human‐related activities. Understanding the processes governing the relations between discharge and suspended sediments is essential for the management of river catchments and river networks. Here, we use the method of wavelet transformation to identify the time‐scale dependency of suspended sediment patterns concerning the temporally and spatially uneven transient processes of sediment production, accumulation, and transport. We analyze the temporal variation of concurrent discharge and suspended sediment fluxes for the Upper Changjiang (Yangtze River, China) at Pingshan station by using a long‐term database collected for over 50 years. Furthermore, we bridge the limitations of pure predictive models to learn from temporal data structures with the main purpose of identifying the mechanisms underpinning the suspended sediment patterns (e.g., climatic forces). Intraseasonal‐to‐seasonal, annual, and inter‐annual dominant time‐scales are thus identified. The short time‐scales are driven by the bi‐modal seasonal precipitation pattern specific to the climate of the region and provide a continuous supply of sediments to the river. The large time‐scales, controlled by high magnitude flow events and within‐reach sediment storage, display alternating periods of increasing and decreasing sediment fluxes; ultimately, they maintain the river channel within balance or within a moderate positive sediment accumulation process. This analysis and methodology help to understand temporal sediment dynamics, and ultimately to manage river catchments.
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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 | 0.001 |
| 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) | 0.007 | 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".