Characterizing scale‐ and location‐specific variation in non‐linear soil systems using the wavelet transform
Bibliographic record
Abstract
Summary The combined action of physical, chemical and biological soil processes, which occur at different intensities and scales, causes complex spatial variation in soil that is difficult to characterize. The presence of non‐stationarity and non‐linearity increases this complexity. Wavelet transforms have been used to analyse non‐stationary soil spatial variation. In this study we used the wavelet transform to characterize the spatial variation of non‐linear soil systems. In the wavelet transform, a mathematical function (mother wavelet) is used to examine the frequency behaviour of a spatial series by translating or dilating the function. The effective resolution in identifying the frequency is dependent on the translation‐dilation parameter, which is further dependent on the central frequency of the mother wavelet. The central frequency of the commonly used mother wavelet (Morlet) has been modified in this study to capture up to 95% of the uncertainty in identifying frequency components present in spatial series. Increased central frequency resulted in more oscillations within a localized window and thus provided enhanced frequency resolution, which helped to identify the instantaneous (≡ localized) frequency ( IF ). Identification of the IF can reduce the local unpredictability and enable characterization of non‐linear systems. We have demonstrated the method with a case study using soil water storage and clay content data. The wavelet spectra and the wavelet IF spectra provided improved spatial resolution in identifying the dominant frequency (scale) of variation in the spatial series. Information on dominant scales can be used for scale‐specific prediction of soil properties and multiscale soil mapping and modelling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".