Evaluation of multivariate Gaussian transforms for geostatistical applications
Bibliographic record
Abstract
Abstract Traditional geostatistical simulation techniques rely on the assumption of multi-Gaussianity. Although the normal score transform is widely used to convert data to a Gaussian distribution, it only guarantees that the normal scores will be univariate Gaussian and the variables may still have complex multivariate relationships. For this reason, multi-Gaussian transforms became popular for simplifying multivariate geostatistical modelling. This study evaluates three multi-Gaussian transforms: flow transformation, projection pursuit multivariate transform, and rotation based iterative Gaussianisation. Three two-dimensional synthetic case studies were designed with complex multivariate relationships to make it difficult to produce good multivariate Gaussian distributions. The quality of the fitted transforms, the forward transformation of data from the same population and the back transformation from a standard multivariate Gaussian distribution were assessed based on statistical indices and visual inspection. The methods were also evaluated using a real case study with eight variables from the Prominent Hill copper deposit in South Australia. The effects of multi-Gaussian transforms on the reproduction of variograms, univariate and bivariate statistics were qualitatively and quantitatively investigated.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".