Simulation of a Structurally-Controlled Gold Deposit using High-Order Statistics
Bibliographic record
Abstract
The algorithm for conditional simulation based on spatial high-order statistics is applied to a drilling dataset obtaine d from a structurally complex gold deposit, the Apensu deposit in Ghana. Spatial high- order statistics allow capturing nonlinear spatial features of the gold mineralizati on that variograms and covariances cannot. Since robust spatial high-order statistics cannot be inferred only from scattered samples, they are borrowed from a training image. In this case, sequential Gaussian simulation with local var iograms within domain boundaries is used to build a training image. At di fferent locations HOSIM uses the spatial high-order statistics to approximate no n-Gaussian distributions of possible values conditioned by neighboring data. Th e effect of sampling clustering in the probability distribution and its statistics is taken into account by incorporating declustering weights in the inference of low and high-order statistics required by high-order simulation. The resulting re alizations reproduce the cdf and the low-order statistics of data and tend to approa ch the high order statistics of the training image. They also reproduce the gold-rich m ajor and well sampled structures. The reproduction of small structures an d undersampled is hindered by the use of a Gaussian based training image and the similitude of their gold grade populations to those of the background host rock.
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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 | 0.000 | 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 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".