Caving resources and reserves: Defining a process for understanding variability and appropriate classification across global codes through project life
Bibliographic record
Abstract
Caving Ore Reserves have unique modifying factors, including cave growth and material mixing, that need to be considered above and beyond the typical factors for other deposits. In this paper, we look at the confidence of Resource classification process based on that outlined by Parker (2014). With increased computing power and conditional simulation, Resource estimation can now simulate multiple block models based on the same drill holes, allowing the variability of the Mineral Resource to be estimated. However, there was no process available to discuss how to apply modifying factors of block cave and sub level caves. A process for estimating some of the applicable modifying factors by using simple macros in mine planning software, so that conditional simulation can be added to mixing models to determine the variability across these mixing models, is proposed. The Resource can then be classified based on this resource estimation technique. As the mine sources more data from markers or flow calibration, the error from the Mineral Resource model can be separated from the error in the mine planning flow models – this paper discusses an application of the f factors defined by Parker (2014). We explore the intricacies of the different Codes for reporting in Canada (NI43-101), the USA (S-K 1300), SAMREC and Australia (JORC). We look at the treatment of Reserves at different stages of a cave's life from the planning phase (PFS and FS), through to cave ramp-up, and finally during the production phase. The key considerations of each phase of a cave's life in terms of Reserve reporting, the key differences in each reporting Code for each phase of a cave's life, and a method for communicating variability through time between the resource and reserve model are discussed.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".