Development of a new method of wavelet aided trend detection and estimation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The detection and estimation of trends in the presence of noise, periodicities, or discontinuous patterns is important in hydrology and climate research studies. The basic idea of currently available trend estimation techniques (tests) is that the trends should be smooth and monotonic; however, hydro‐climatologic variables contain multiple signals, and have segments of increasing and decreasing trends. As a result, estimating trends in time series is an essential but arcane art and it is therefore important to continue developing the theory and practice of trend analysis. In this paper, a new technique is proposed based on the continuous wavelet transform (CWT). CWT permits the transformation of observed time series into wavelet coefficients according to time and scale simultaneously. These coefficients can be used to detect and estimate trends or to reconstruct signals that are of interest. The proposed CWT method was first tested on computer‐generated data exhibiting both periodic and noise components. It was then applied to observed monthly minimum streamflow observations extracted from the Reference Hydrometric Basin Network (RHBN) for five different eco‐zones in Canada. It was concluded that the proposed wavelet transform (WT) based method provides a very flexible and accurate tool for detecting and estimating complicated signals. The results from monthly minimum observations indicate that short period fluctuations are decreasing, while multi‐annual variability is increasing in Canada. And finally, a persistent ∼55‐year signal is well correlated with the Pacific Decadal Oscillation in all records, which indicates that trends are not controlled by a single factor. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.000 |
| 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 it