Uncertainty Modeling of a Vein Deposit
Bibliographic record
Abstract
This thesis sets out a workflow to quantify uncertainty of the Arrow Deposit, a tabular, vein type, high-grade, basement-hosted, uranium deposit located on NexGen Energy Limited’s 100%-owned Rook I property in northern Saskatchewan. The uncertainty associated with the volume, grade, and density variables of the deposit are the focus of the study, as these variables define the overall metal content of the deposit, the largest input to project economics. In the study domain, the grade variable is exhaustively sampled at all drill hole locations, but the density variable is missing at approximately 82% of the drill hole locations and biased to high-grade intercepts. Effort was taken to make an interim debiased homotopic representative dataset through a simple imputation process that selected density values from the global deposit-wide dataset. Two uncertainty models were created for the grade and density variables using the representative dataset: a multivariate spatial bootstrap model and a density-imputed model using a Gaussian Mixture Model built on the representative dataset, followed by decorrelation via Projection Pursuit Multivariate Transformation and independent grid Sequential Gaussian Simulation. The multivariate spatial bootstrap model provided an assessment of uncertainty of the histogram parameters while considering the spatial correlation between the variables at data locations. The imputation and simulation process features the transfer of the uncertainty associated with the missing values of the original dataset while accounting for the biased nature of the dataset. Volume uncertainty was quantified by assessing: the uncertainty of the thickness perpendicular to the plane of continuity via a geometry imputation process, and the uncertainty of the boundary in the plane of continuity via an indicator estimate. The two assessments of domain uncertainty were combined to output volume uncertainty. The volume, grade, and density uncertainty models were combined into a single model; this uncertainty model will be used as the basis to evaluate mine output uncertainty with the mine design as the transfer function. The metal content of the Arrow Deposit is most sensitive to changes in volume due to the extreme high-grade nature of the deposit, therefore the Boundary model, which has a large range of possible volumes (and range in the determination of ore/not ore), contributes the most to the metal content uncertainty. The Grade/Density model also significantly contributed to the metal content uncertainty, which is credited to the short-range variability of the variables. The thickness perpendicular to the plane of continuity model provided additional uncertainty to the metal content, but to a much lesser degree as the range of possible volumes associated with the model were relatively small.
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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".