Wavelet Analysis of Tide-affected Low Streamflows Series
Why this work is in the frame
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Bibliographic record
Abstract
In certain rivers that drain very flat terrains in coastal areas, the streamflow series observed at a flow-gauging station may come under the direct influence of the backwater effects of tides. The phenomena may be negligible under conditions of high flows but can be critical under some extreme low-flow conditions. The errors in low flow estimation are large if a proper de-noising is not implemented to remove the effects of the tidal effects. Scrutinizing the hydrologic time series using a standard time-frequency domain based Fourier transform methodology cannot resolve conclusively the sources of the noise. However, a new perspective can be obtained by using a wavelet transformation to analyze the time series in the time-scale domain. By using this approach, a case study involving a streamflow series observed at Kapit, Sarawak, Malaysia yielded conclusive evidence of the influence of tides at the flow-gauging site during the low flow period. Upon confirmation that the noise is indeed of tidal origin, the observed water level series was subjected to an appropriate wavelet-based de-noising procedure to derive a smoothed series. Then, together with an established rating curve, a de-noised discharge series could also be approximated. Low-flow quantiles were subsequently derived by fitting a suitable frequency distribution to the annual minimum series abstracted from the de-noised discharge series. The methodology presented illustrates the potential of using wavelet analysis methods in solving other similar problems.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.004 | 0.001 |
| 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 it