Some common flaws encountered in mineral resource estimation and how to avoid them
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it