Kriging data with measurement error: A review and a generalized approach
Bibliographic record
Abstract
Filtered kriging with parametric error (FKPE) is a mathematically sound method that generalizes and addresses the oversimplifications of previous kriging algorithms designed to filter error. The proposed approach is developed for handling grade-dependent (heteroscedastic), non-stationary, and spatially correlated sampling errors. The covariance between each pair of measurements or nodes is estimated from their model of errors and the spatial continuity of the underlying process. This research is driven by the fact that sampling and analytical errors are inherent in the samples used in the mining industry. In recent decades, data from quality control programs monitoring these errors have become widely available. FKPE handles more types of errors than other kriging algorithms to filter error and, for any number of subsets, it avoids jointly modelling the models of co-regionalization required by co-kriging methods. The precision gains of FKPE over other methods depend on how well the error model is fit to the data. Therefore, a detailed analysis of the error model and how to estimate its components is discussed. In a five-data toy example with high independent and grade-dependent error, kriging methods with oversimplified error models overweighted a high-grade datum by 50% compared to FKPE estimates. The performance of FKPE and other methods is illustrated by two synthetic examples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".