Constructing empirical resolution diagnostics for kriging and minimum curvature gridding
Bibliographic record
Abstract
Abstract Resolution analysis is a crucial appraisal procedure in solving general estimation problems, especially for correctly interpreting the results of spatial analysis schemes. Resolution analyses based on the resolving kernels are typically applied to small inverse problems only when the inverse operators are explicitly accessible. Stochastic simulation schemes have been proposed to extract empirical resolution information for solving large inverse problem. In this study, we generalize the formulation of the empirical resolution length and derive the characteristic length of the point spread function for general estimation methods such as minimum curvature gridding and kriging interpolation schemes that are not equipped with explicitly accessible resolving kernels. The implementation of these resolution diagnostics has not been possible in the past and is demonstrated in this study to facilitate clarifying the advantages and limitations of these widely used methods. In addition, we compare these schemes, based on the resolution appraisal, with a multiscale gridding algorithm in the spatial analysis of the Pacific seafloor heat flow observations. By depicting the pattern of the resolution length variations of both the empirical averaging function and the point spread function for each of the estimated models, we demonstrate that schemes equipped with multiscale capability are more favorable for accommodating sparse, nonuniform data distribution than stationary schemes, such as the kriging method. Furthermore, the empirical resolution pattern constructed in this study facilitates the selection of an appropriate reference function and radii of influence for fitting the variogram, which is difficult but critical when using the kriging method.
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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 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".