Bibliographic record
Abstract
Abstract Companies listed on the big six markets (Australia (ASX), Canada (TSX), USA (NYSE), UK (LSE Main Market and AIM), Singapore (SGX Main Market and Catalist) and Hong Kong (HKEx)) all have requirements of some form when it comes to reporting oil and gas reserves and resources. The requirements may be specified by the market rules, the market financial regulators, extra-territorial legislation, accounting standards or standards published by technical societies and are usually supported by Government Legislation. The requirements vary considerably and place very different technical and administration demands on oil and gas companies. This paper provides an engineer's broad brush overview of the various requirements, provides a comparison to SPE PRMS, makes a qualitative comment on the strength and weakness of the exchange rules and ultimately provides some best practice considerations. This paper can benefit all listed companies who need to know the rules they are required to comply with, can benefit all unlisted companies by giving them an insight into the types of reporting that investors may typically demand and can benefit petroleum professionals by enabling them to know what they must provide.
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.
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 | 0.001 | 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".