Some common flaws encountered in mineral resource estimation and how to avoid them
Bibliographic record
Abstract
Preparation of a mineral resource estimate (MRE) is an essential component in the mining cycle, as errors that occur in an MRE will affect all following steps that rely upon its accuracy. Over the course of many decades, SLR Consulting (Canada) Ltd. and predecessor Roscoe Postle Associates have observed a number of common errors that occur at all stages of the workflow. The purpose of this paper is to share some of SLR’s experiences relating to the errors encountered during the preparation of MREs and to present some solutions for avoiding these errors. SLR observes that the source of many of the flaws is the result of the level of knowledge, experience, judgment, or expertise by the practitioner of the fundamental principles of mineral resource estimation and with the software package used in preparing the MRE. Attention to detail and adherence to high quality standards throughout the estimation process is the first step in avoiding many of the errors. A critical item for all practitioners to bear in mind is that they are accountable and bear the ultimate responsibility for all aspects of their work.
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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 | 0.073 | 0.239 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".