Bibliographic record
Abstract
Advisory Management Board 14 Africa 85 agriculture, UK, and carbon emissions 209, 210 Algeria, gas in 73, 83, 84 aluminium industry, UK, emission reduction 216 Argentina, gas in 73 Australia 18, 38, 82, 83, 84 electricity in 38 gas in 82, 83, 84 New South Wales comparative analysis 18 Austria, gas in 75, 83, 84, 85 Avenis 63-4 Axpo 63 Balancing and Settlement Code (BSC) 10 Belgium, gas in 73, 75, 83, 84 benchmarking versus yardstick competition 52-5 Beveridge, William 176 Bogetoft mechanism 40-42, 53-4, 58 see also Schleifer mechanism Bös and yardstick competition 55 Bosnia-Herzegovina, gas in 83, 84, 85 brick industry, UK, emission reduction 216 British Coal 90, 91, 92-3 British Gas 2-3, 4, 7 BSC (Balancing and Settlement Code) 10 Bulgaria, gas in 83, 84 Burns and Weyman-Jones comparative efficiency study 57 Canada, gas in 73, 74, 83, 84, 85 carbon dioxide emissions developing countries decomposition in 193-6 and income relationship 189-96 possible growth in 198-201 and environmental Kuznets curves 175-205 industrialized countries decomposition in 185-9 and income relationship 179-85 patterns of growth in 196-8 Kaya identity and 185-9, 193-6, 197, 199 targets, UK see UK, emissions targets CCGT (combined cycle gas turbine) stations 7, 8, 92, 93, 182 CCL (Climate Change Levy) 208, 210-12 CDM (Clean Development Mechanism) projects 200 CEGB (Central Electricity Generating Board) 5, 7, 9 cement industry, UK, emission reduction 216 Centrica 4 ceramics industry, UK, emission reduction 216 chemicals industry and carbon emissions 209, 210 emission reductions 216 China carbon dioxide emissions growth trends 198-201 and income 189 , 190, 193 per capita 189, 198 GDP 194, 195 population 194, 196 primary energy intensities 193, 194, 195 use 190 Clean Development Mechanism (CDM) projects 200 Climate Change Levy (CCL) 208, 210-12
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.846 | 0.760 |
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".