Bibliographic record
Abstract
This paper describes a technique to predict the head grades and likely variances from target grade that can be achieved from mining an iron ore deposit. The method develops a production schedule from a block model. The example used was created from a wide-spaced drilling program and was classified as an Inferred Resource, but the method can be applied to any block model based on the available combination of Proved and Probable reserves. Marketing contracts require examination whether the target grade, i.e. Fe, P, SiO2, Al2O3, can be kept constant, or must be revised during the life of the mine. A model (written in Excel using Visual Basic macros, details available from the first author) develops a feasible, close to optimal, mine plan from block model data. Cut-off grades for minable ore are chosen. The model evaluates ore tonnage, grade and stripping ratio against depth. Outputs at this stage include plots of slices through the ore body, at nominated x, y and z coordinates, showing ore grade, waste, drill-hole locations and topography. The model then searches for a feasible satisfactory mine plan. The initial grade target is the average for the identified ore blocks. The available block list (ABL) at any time is the set of ore blocks, any one of which can be mined without removing any other ore block. The entire initial ABL has an unacceptably large total stress, defined as [(Grade–Target)/Tolerance]2, summed over the four relevant minerals. The model trims the ABL, removing the block most harming the total stress. Trimming is repeated until the Total Stress reduces to an acceptable level. The trimmed ABL defines the first mine campaign. Removing the trimmed block set, a second ABL is exposed, which is again trimmed and mined. The process is automatic: a few seconds computation provides a complete mine plan. Re-running the model with adjusted grade limits quickly shows whether a uniform target can be achieved or whether the target must be modified during the mine life.
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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) | 0.000 | 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".