Bibliographic record
Abstract
Academia From June 2014 to January 2015, global crude oil prices dropped by almost 60%, from about USD 108/bbl to USD 46/bbl. There were many factors that drove these prices downward. Being an integral part of the industry, it is important that young professionals understand the governing principles and be able to connect them with facts. The following are some of the prominent factors responsible for the drastic change in oil prices. Supply and Demand. According to the December 2014 monthly update of the United States Energy Information Administration, the global supply of liquid fuels increased by 1.8 million B/D to 92 million B/D in 2014 while demand did not keep pace. Domestic oil production in the US increased to 8.8 million B/D last year, the highest level in 30 years, and US crude oil inventory levels have reached an 80-year high. Meanwhile, US demand for oil declined from what had been a 10-year high. Economic. Strong economic growth translates into higher energy usage and can impact prices positively. But the converse is also true, in that weak economic indicators could represent lower energy consumption and, consequently, lower prices. The US, China, Japan, and India are the world’s top consumers of crude oil. Europe consumes 22% of the world’s oil. Meanwhile, Europe’s largest producer of crude, the United Kingdom, became a net importer in 2013. Economic indicators for all these countries are intensely watched daily as traders attempt to determine the future direction of the price of oil and countries become intrinsically tied to a global economy—one that can be very fickle in nature. Political. Energy will always be a controversial issue pitting producers against consumers and environmental groups. A recent example of this is the Keystone XL oil pipeline project in North America. The planned route from Canada’s Alberta province to Cushing, Oklahoma, has run into issues regarding a required international border permit, landowner rights, and environmental concerns over the process that produces the crude oil itself.
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 | 0.002 | 0.001 |
| 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".