Defining Uncertainty: Comparing Resource/Reserve Classification Systems for Coal and Coal Seam Gas
Bibliographic record
Abstract
Transparent, objective, and repeatable resource assessments should be the goal of companies, investors, and regulators. Different types of resources, however, may require different approaches for their quantification. In particular, coal can be treated both as a solid resource (and thus be mined) as well as a reservoir for gas (which is extracted). In coal mining, investment decisions are made based on a high level of data and establishment of seam continuity and character. The Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (the JORC Code) allows deposits to be characterised based on the level of geological and commercial certainty. Similarly, the guidelines of the Petroleum Resource Management System (PRMS) can be applied to coal seam gas (CSG) deposits to define the uncertainty and chance of commercialisation. Although coal and CSG represent two very different states of resources (i.e., solid vs. gaseous), their categorisation in the JORC Code and PRMS is remarkably similar at a high level. Both classifications have two major divisions: resource vs. reserve. Generally, in either system, resources are considered to have potential for eventual commercial production, but this has not yet been confirmed. Reserves in either system are considered commercial, but uncertainty is still denoted through different subdivisions. Other classification systems that can be applied to CSG also exist, for example the Canadian Oil and Gas Evaluation Handbook (COGEH) and the Chinese Standard (DZ/T 0216-2020) and both have similar high-level divisions to the JORC Code and PRMS. A hypothetical case study of a single area using the JORC Code to classify the coal and PRMS for the gas showed that the two methodologies will have overlapping, though not necessarily aligned, resource and reserve categories.
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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".