Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools
Bibliographic record
Abstract
Abstract Soil erosion induces soil redistribution within the landscape and thus contributes to the spatial variability of soil quality. This study complements a previous experimentation initiated by the authors focusing on soil redistribution as a result of soil erosion, as indicated by caesium‐137 (137Cs) measurements, in a small agricultural field in Canada. The spatial variability of soil organic matter (SOM) was characterized using geostatistics, which consider the randomized and structured nature of spatial variables and the spatial distribution of the samples. The spatial correlation of SOM (in percentages) patterns in the topsoil was established taking into account the spatial structure present in the data. A significant autocorrelation and reliable variograms were found with a R2 ≥ 0·9, thus demonstrating a strong spatial dependence. Ordinary Kriging (OK) interpolation provided the best cross validation (r2 = 0·35). OK and inverse distance weighting power two (IDW2) interpolation approaches produced similar estimates of the total SOM content of the topsoil (0–20 cm) of the experimental field, i.e. 211 and 213 tonnes, respectively. However, the two approaches produced differences in the spatial distribution patterns and the relative magnitude of some SOM content classes. The spatialization of SOM and soil redistribution variability – as evidenced by 137Cs measurements – is a first step towards the assessment of the impact of soil erosion on SOM losses to recommend conservation measures. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".