Bibliographic record
Abstract
97-8 Arun, Indonesia 96, 102 Asia 12-13 Central 69 see also under individual countries Atlantic Basin emerging LNG market in 76-9 GECF 72, 73 Atomic Energy Canada Ltd (AECL) 174, 176 Azerbaijan 70 Baker Institute World Gas Trade Model (BIWGTM) 92-103 Baltic states 172, 178-85 battery electric vehicles (BEVs) 249-50, 271, 275-6 Belarus 59, 62, 74, 81 biofuels 192, 260, 275-6 biological processes 207 hydrogen production 212-14 biological water-gas shift reaction 213 biomass 192, 207-8, 215, 216 hydrogen production 210-11, 212-14, 235 biophotolysis 212-13 bipolar electrolyser 208 Blue Banana region 243-5 Bolivia 97 Brazil 97, 103 British Energy 146, 152, 155, 159, 161, 164 fi nancial crisis 155, 156-8, 162 risk management strategies 162, 163 share price 156, 157 British Gas 10 Bulgaria 177 buses, hydrogen 255, 278 byproduct hydrogen 234, 235-6, 253 CAFE policies 263-4 California energy crisis of 2000-2001 25, 26-8, 51 zero emission programme 251 Canada 76-7 CANDU-6 nuclear technology 174-5 capacity credit 202 carbon capture and storage (CCS) technology 19, 111, 193, 259, 263, 271 carbon dioxide emissions 84-5, 199 reduction target 261 see also greenhouse gas emissions carbon dioxide price risk 124-5, 143-4 cartels 300 gas 4, 72-3, 100-101, 257 cash-fl ow analysis 241-3 Caspian region 70-71, 82 catastrophe risk 163, 164 Central Asia 69 Central Europe 11, 18, 110, 171 Centrica 10 Cernavoda power plant complex 174, 175-6 Chernobyl disaster 164, 179
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.842 | 0.766 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".