Bibliographic record
Abstract
Values of net oil imports(‐)/exports for Austria, Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, Japan, Canada, the United States and Australia. Updated on a monthly basis. Number of imports for Belgium, France, Germany, Greece, Republic of Ireland, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, and Other EU‐15 (EU‐15), Norway, Poland, Switzerland, Turkey, and Other Europe (OECD Europe), Canada, Chile, Mexico, and the United States (OECD Western Hemisphere), and Australia, Japan, Republic of Korea, New Zealand, (OECD Asia‐Pacific), and Total OECED. Current data for Austria, Belgium, Finland, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, Other EU‐15 (EU‐15), Czech Republic, Hungary, Poland, Slovakia, Turkey, and Other Europe (OECD Europe), Canada, Mexico, and the United States (OECD Western Hemisphere), Australia, Japan, Republic of Korea, and New Zealand (OECD Asia‐Pacific). Updated on a monthly basis. Current data for principal importers of natural gas and the amount in which they import from United States, Republic of Korea, United Kingdom, France, Japan, Italy, Germany and Spain (Table 19.1). Current data for principal exporters of natural gas and the amount they export to Canada, Trinidad and Tobago, the United States, Netherlands, Norway, Russia, Turkmenistan, Qatar, Algeria, Nigeria, Indonesia, Malaysia and Australia (Table 19.2). Updated on a monthly basis.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.396 | 0.356 |
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".