Trend 2006 - 2014. Energy Information Administration. Crude Oil Imports: Crude Oil Imports | Country: USA | Country*: Canada, 2006-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 004-010-001.
Bibliographic record
Abstract
Energy Information Administration (2015). Crude Oil Imports: Crude Oil Imports | Country: USA | Country*: Canada, 2006-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 004-010-001. Dataset: Barrels of crude oil imported into the US, as reported monthly by importing companies. Approximately 250 companies submit reports each month. Data are shown in thousands of barrels. This dataset includes data on crude oil imported into the US, as reported by importing companies on Form EIA-814, as required under the Federal Energy Administration Act of 1974. Approximately 250 companies submit reports each month. The EIA site referenced above includes data on other petroleum product imports and ports of entry. Category: Energy Resources and Industries Source: Energy Information Administration The Energy Information Administration (EIA), created by Congress in 1977, is an independent statistical and analytical agency within the United States Department of Energy. Its mission is to provide policy-independent data, forecasts, and analyses to promote sound policy making, efficient markets, and public understanding regarding energy and its interaction with the economy and the environment. http://www.eia.doe.gov/ Subject: Energy, Fuels, Petroleum, Imports, Crude Oil
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.083 |
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".