Bibliographic record
Abstract
US is one of the major consumer of Oil in the world, and after US, China, Japan and then India come on second, third and fourth position respectively. Out of the total demand of the oil in the world 60% oil is consumed by only 10 countries namely US, China, Japan, India, Russia, Brazil, Saudi Arabia, Germany, Korea and Canada. It indicates the dependency of economies on the oil. Crude oil has become an important input in almost each and every unit that are running in the country including farming, mining and manufacturing. Health of all the developed and developing countries is depended heavily on the price of crude oil price. Crude Oil has been traded throughout the world and there prices are behaving like any other commodity as swinging more according to demand and supply. In the short term, prices of crude oil is influenced by many factors like socio and political events, status of financial markets whereas, from medium to long run it is influenced by the fundamentals of demand and supply which thus results into self price correction mechanism. Crude oil plays an important role in the economy of every country. Present study is attempted to discuss the importance of crude oil price and it‘s role in the economy. The study also highlighted the current trends of overall demand and supply of the crude oil in the world.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".