Some basic improvements in mineral resource estimation and reporting
Bibliographic record
Abstract
Bulk density miscalculations and subsequent reporting can produce resource estimation errors and unexpected financial outcomes. Density and bulk density vary throughout a mineral deposit, especially in porous deposits and deposits containing variable amounts of high-density minerals (e.g., barite). Resource estimators must correlate bulk density with core recovery and apply quality assurance/quality control analysis to bulk density data. Core recovery is an important factor in resource estimation and reporting. Block tonnages (ore and waste) are estimated from volumes using a dry bulk density value. Ore dilution is a volumetric effect integral to ore reserve estimation that also has the potential to affect the economics of mining a deposit. Both the bulk density and dilution problems could be overcome by reporting a mineral resource estimate in grade per unit volume.
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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 | 0.047 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.045 |
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".