Bibliographic record
Abstract
The year 2012 produced a number of headlines in energy-related news. Some of the trends reported are part of long-term changes in the world energy landscape, others show adaptations to shorter-term disruptions. The United States experienced a large increase in oil and gas supplies, while in China, Germany, and Japan—together representing a quarter of global gross domestic product (GDP)—renewables captured a greater share than nuclear power. Liquefied natural gas (LNG) trade, on the other hand, declined for the first time since coverage of gas trade began 30 years ago. And record amounts of coal, exiled from the United States by the shale gas revolution, were shipped to Europe. Beyond those headlines, energy developments looked unsurprising. Consumption growth slowed to 1.8 percent, below its 10-year average, for all fuels (bar renewables and hydropower) and in all regions except Africa—quite in line with a lackluster global economic performance overall. The headline figures looked relatively normal, but the energy system is good at adjusting to a changing world, and sometimes it drives change. This long-term context is the best starting point for assessing the data trail of a year in which energy markets appeared calm but actually involved lots of adjustments.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".