Long-term temporal structure analysis of sediment fluxes in the Upper Changjiang (Yangtze River) using a wavelet transformation
Bibliographic record
Abstract
Temporal fluctuations of suspended sediment fluxes are highly related with the flow and sediment regime of river networks. Flow dynamics are mainly triggered by climatic forces. Conversely, sediment regime is majorly controlled by land cover/land use changes and by human infrastructures (e.g. dams). Resulting sediment fluxes thus show a non-linear and non-stationary nature. The wavelet transformation technique emerges as a skilful tool capable of dealing with this type of data. It allows to distinguish within the full spectrum of time-scales, those which are dominant and thus govern the overall trend of the time-series. Furthermore, the temporal multiresolution wavelet technique allows to accurately denoise the discharge and SSC time-series, filtering out the high frequency intermittent physical processes which are superimposed on the main signal. In this research, we analyse a long-term dataset of discharge and sediment fluxes time-series for the Upper Changjiang (Yangtze River, China).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 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".