Worldwide Crude Oil Production Capacity, Supply, and Demand: What is Your Baseline?
Bibliographic record
Abstract
Abstract Reasonably accurate information of worldwide crude oil production capacity, supply and demand (CSD) is crucial to management and investment decisions in the energy industry in particular and the economy in the aggregate. As the industry relies on consistently flawed reporting processes, the industry encounters dynamics outside those normally experienced in a commodity industry. The International Energy Agency (IEA) is the primary source of the public data. The IEA was created in 1974 as the response to the 1973 Arab oil embargo of 16 member countries of the Organization for Economic Cooperation and Development (OECD). This paper will help the reader to understand the IEA's organization and some of the inaccuracies inherent in the reporting processes. This understanding should assist those using the data to develop better forecasts and should be of interest to those seeking to improve the reporting process. Coupled with this investigation will be a brief glance at the historical worldwide crude oil supply, capacity and demand and the corresponding price trends. The reader will gain a tool for enhancing their strategic planning in addition to increasing the accuracy of price forecasts by seeing the relationships between recent price trends and the IEA reports of capacity, supply and demand,
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".