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Record W2332341516 · doi:10.2118/0215-008-twa

Turbulent Commodity Prices and the Turmoil for Young Professionals

2015· article· en· W2332341516 on OpenAlexaboutno aff
Tom Seng

Bibliographic record

VenueThe Way Ahead · 2015
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsCrude oilAgricultural economicsCommodityChinaPaceEconomicsConsumption (sociology)Petroleum industryPeak oilBusinessEconomyMarket economyGeographyClimate changeEngineering

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.007
Scholarly communication0.0150.010
Open science0.0010.010
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0400.014

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.

Opus teacher head0.042
GPT teacher head0.323
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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