Bibliographic record
Abstract
An analysis of petroleum unitization legislation from 90 selected jurisdictions has been structured through reference to the adopted tripartite legislative framework of statutes, implementing regulations and petroleum production contracts (PPCs). This has been done by grouping the jurisdictions according to the occurrence of discovered legislation at one, two or all three levels of the adopted framework, thereby giving rise to seven groups. The selection of two lead jurisdictions from each group has been used to create a kernel for the analysis of unitization legislation for that group. The 14 jurisdictions thus selected were Angola, Canada, Côte d’Ivoire, Indonesia, Madagascar, Norway, Oman, Pakistan, Peru, Thailand, Turkmenistan, Trinidad and Tobago, Tunisia and the United Kingdom. A comparison of unitization legislation across these 14 jurisdictions has shown that no two jurisdictions have the same legislation for petroleum unitization. The analysis has reinforced the view that from a unitization legislative standpoint, no jurisdiction has got it right.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".