Application of Volume Uncertainty for Resource Classification: A Case Study on the Rondon Do Pará Bauxite Deposit, Brazil
Bibliographic record
Abstract
This study illustrates the application of conditional simulations to calculate the uncertainty associated with the thickness of bauxite ores. The bauxite deposit of Rondon do Pará in northern Pará State, Brazil, is characterized by a well-defined lateritic profile, with the ore being composed of two sequential horizons: massive bauxite and ferruginous bauxite. This study used ore thickness data from 1.005 drillholes with different grid spacing. Drillhole intervals of both types of bauxite ore were accumulated, converting the database from 3D to 2D. Sequential Gaussian simulation produced probability maps calculated from certain confidence intervals, which permits obtaining the uncertainty associated with estimates in thickness. Results show that in portions with the same regular drillhole spacing there are different ranges of uncertainty and variability, which could be useful to support resource classification, associating different confidence intervals to resource classes. This analysis could also guide the drilling program for resource conversion in order to optimize costs, indicating areas where there is greater uncertainty and would need to be densified. The incorporation of this information into the resource model could be very helpful for supporting subsequent studies of economic evaluation and risk analyses with respect to this type of deposit or similarly in mineral exploration.
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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) | 0.000 | 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 | 0.000 |
| 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 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".