Spectral Analysis of Tillage‐Induced Differences in Soil Spatial Variability
Bibliographic record
Abstract
Tillage research has traditionally focused on mean effects; few studies have compared treatments in terms of their spatial variability. We applied several methods of time‐space series analysis to investigate the spatial variability of gravimetric water content (w), volumetric water content (θ), bulk density (ρ b ) and total C (TC) in the upper 10 cm of long‐term conventional‐till (CT) and no‐till (NT) soil management practices at Lexington, KY. The soil was a Maury silt loam (fine, mixed, semiactive, mesic Typic Paleudalf). Replicate transects were established in untracked interrows parallel to the direction of tillage in the CT practice. Each transect was 48.5 m long with 107 equally spaced sampling points. Soil spatial variability was higher under NT than under CT. Spectral analyses of variance identified significant differences between tillage treatments at frequencies <0.02 cycles m −1 for all soil properties, and at 0.18 cycles m −1 for w, 0.13, 0.31 and 0.48 cycles m −1 for θ, and 0.22, 0.70 and 0.90 cycles m −1 for ρ b Spatial variations in water content and ρ b appeared to be related to the distribution of TC. Coherency analysis indicated relationships were strongest at frequencies <0.09 cycles m −1 For NT the relationship between w and TC was also significant at higher frequencies (1.01–1.05 cycles m −1 ). Gravimetric water content increased as TC increased, while θ and ρ b decreased. Lagged relations for w versus TC were more frequent in CT than NT, possibly due to soil translocation during tillage operations. The opposite was true for θ versus TC and ρ b versus TC, suggesting that soil aggregates form at some distance from sites of carbon deposition under NT.
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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 | 0.001 |
| 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 | 0.000 | 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".