Bibliographic record
Abstract
The sudden effect of crude futures oil refers to specific events that lead to significant fluctuation in the price of the futures.Event analysis on major occurrences paves us a path to avoid these failures.This article aims to investigate the causality of event-related price vacillations on crude oil.Specifically, this study inspects recent major crude oil price drop incidents, including the Yuan You Bao Failure, the Russia-Saudi Arabia oil price war, and the Suez Canal obstruction.The causes range from human errors on policy differences to naturally occurred disasters and national conflicts.In detail, the Yuan You Bao failure results from a mistake on the Bank of China's financial product dropping below 0 in the Chicago Mercantile Exchange.Besides, the sudden disagreement on supply and demand gives rise to the revenging increase in crude oil production between Russia and Saudi Arabia.Additionally, the Suez Canal Obstruction is ascribed to Ever Given's container ship running aground and blocking the canal after an unpredicted sandstorm.Apart from the events' unpredictability, it finds general flaws in human regulations and market supervision.The comprehensive analysis indicates the importance of law implementations relating to policy regulations on crude oil prices.These results shed light for investors to understand specific events' effects on making decisions and offer solutions for the government in emergency circumstances.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".