Separating scale-specific soil spatial variability: A comparison of multi-resolution analysis and empirical mode decomposition
Bibliographic record
Abstract
Soil spatial variability is scale dependent. In separating soil spatial variability at multiple scales, wavelet based multi-resolution analysis (MRA) is an established method, whereas empirical mode decomposition (EMD) has just been introduced in soil science. A careful comparison between these methods is necessary and is the goal of this research. Here a brief description of the methods is provided and they are compared using soil water storage (SWS) data observed along a 576 m transect. The MRA separated the variations of a spatial series into predefined scale intervals, each of which contributed differently to the overall variance of the series. EMD separated the overall variation into different mode functions (known as Intrinsic Mode Functions; IMFs) representing different scales as they are present in the series. The EMD did not use any predefined basis (such as mother wavelet in wavelet transform) for scale separation. The proportion of overall variance contributed at each scale was used to identify the most dominant scale. Correlation between the scale components (MRA products and IMFs) and different factors controlling SWS along the transect enabled identification of the dominant controls of SWS and the scales at which they occur.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".