Decorrelation and Imputation Methods for Multivariate Modeling
Bibliographic record
Abstract
In most mining projects, multivariate modeling of regionalized variables has a critical impact on the final model due to complex multivariate relationships between correlated variables. In geostatistical modeling, multivariate transformations are commonly employed to model complex data relationships. This decorrelates or makes the variables independent, which enables the generation of independent models for each variable while maintaining the ability to restore multivariate relationships through a back-transformation. There are a myriad of transformation methods, however, this chapter discusses the most applied methods in geostatistical procedures. These include principal component analysis (PCA), minimum/maximum autocorrelation factors (MAF), stepwise conditional transform (SCT), and projection pursuit multivariate transform (PPMT). All these transforms require equally sampled data. In the case of unequal sampling, it is common practice to either exclude the incomplete samples or impute the missing values. Data imputation is recommended in many scientific fields as removing incomplete samples usually removes valuable information from modeling workflows. Three common imputation methods are discussed in this chapter: single imputation (SI), maximum likelihood estimation (MLE), and multiple imputation (MI). Bayesian updating (BU) is also discussed as an adaptation of MI to geostatistical analysis. MI methods are preferred in geostatistical analysis because they reproduce the variability of variables and reflect the uncertainty of missing values.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".