Not all barrels are created equal: understanding the difference between standards of regulatory disclosure can impact your investment decisions
Bibliographic record
Abstract
Petroleum industry stakeholders rely on estimates of petroleum reserves and resources as a cornerstone for making informed strategic investment decisions. Whether assessing a property or corporate target in a mergers and acquisitions process, seeking or providing equity or debt financing, developing upstream or downstream projects, engaging in sales contract negotiations or satisfying regulatory disclosure requirements, a clear understanding of the basis of these estimates is critical. Worldwide, several standards of resource estimation are widely accepted (Society of Petroleum Engineers Petroleum Resources Management System (SPE-PRMS), Securities and Exchange Commission (SEC) guidelines and Canadian Oil and Gas Evaluation Handbook (COGEH)) and disclosure requirements depend on the regulatory jurisdiction (i.e. Australia, Australian Stock Exchange (ASX) Listing Rules Chapter 5; USA, SEC Regulation S-K; Canada, NI 51-101). Understanding the differences in these standards is imperative for correctly assessing value, development potential and project risks. Focusing on Australia, the United States of America and Canada, this presentation identifies key differences in these standards, and the potential implications affecting your strategic investment decisions.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".