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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".