Systematic Evaluation of Kriging and Inverse Distance Weighting Methods for Spatial Analysis of Soil Bulk Density.
Bibliographic record
Abstract
Evaluation of Kriging and Inverse Distance Weighting Methods for Spatial Analysis of Soil Bulk Density.Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 55: 1.1-1.13.Spatial interpolation methods are frequently used to characterize spatial phenomena in soil properties over various spatial scales; however, it is very difficult to select the best interpolation method.No specific standards or tests are available to determine the "appropriateness" of an interpolation model.This study focused on evaluation of the performance of two widely used interpolators: kriging and inverse distance weighting (IDW) for the spatial analysis of soil bulk density.Predicted values by both interpolation models were compared with the observed data and analyzed using various indices.Results indicated that both interpolation methods do not reflect true variation of bulk density.Both models, however, performed equally well for spatial analysis with almost the same accuracy, precision and consistency with a difference of less than 1.0%, 0.5% and 2.0%, respectively.Inverse distance weighting method, simpler than kriging method, gives competitive and somewhat superior results when an optimal power value is used.No relation was found among coefficient of variation, skewness and kurtosis in selecting an appropriate interpolation method for spatial description or selecting a power value for IDW method or a semivariogram model for the kriging method.This study has provided an example of an approach to systematically evaluate the performance of one or more spatial interpolation methods.By employing the validation indices used in this study, any interpolation method can be assessed to accurately describe any spatial data set from the field.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".