Determining Spatial Correlation Structure Using a Flexible Method
Bibliographic record
Abstract
ABSTRACT Modelling spatial data using geostatistical methods relies on parametric variograms and covariances. Ordinary, weighted and generalized least square, maximum and restricted maximum likelihood are some methods to estimate spatial processes' variogram (covariogram) parameters. Nevertheless, these methods necessarily do not result in the best prediction values for each desired loss function. This paper introduced a new method to estimate and optimize parameters of the spatial variogram and covariance functions based on a desired loss function to achieve cross‐validated prediction results. The proposed method can be used for different kriging techniques to perform the best prediction values and some desired loss functions such as mean, mean square and mean absolute error and complicated loss function like Linex conveniently. The variogram parameters were estimated under optional desired criteria to control how much they overestimate or underestimate observations. This feature can apply a wide range of controlled conditions to the model. The results indicated the interesting advantages of the suggested workflow versus previous variogram estimation methods. This method provides the best directional variogram, which enhances cross‐validation results when used with generalized least squares as an optimal estimation method for statistical efficiency.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".