Determining Spatial Correlation Structure Using a Flexible Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it