Bibliographic record
Abstract
adaptation economic perspectives 29-31 catastrophic events 32 critique of studies on 51-7 local estimates 33-4 multinational and global assessments 36-45 multiple factors 33 private adaptation 54 regional and national estimates 34-6 research needs 46 sectoral estimates 31-2 importance of 3-4 issues 205-6 additionality 214-20,235-6 agriculture 17,32,35,37,70 Alexeeva-Talebi, V. 108 allocation carbon budget approach 193-7 within EU ETS Directive 258-61 reduction and 203 within sectoral crediting mechanism 224 aluminum production 126 American Clean Energy and Security Act (2009) 270 Andre, Francisco 233 Ansuategi, Alberto 147 Arroyo, Vicki 7,267 Asia, distributional outcomes 133 auctioning within EU ETS Directive 260-61 Australia, distributional outcomes 133 aviation sector 258-9 Barrett, S. 163 baseline setting 214-15, 235,188-93 border adjustment 118 Boston Consulting Group (BCG) study 111-12 Buchner, B. 181 Bush Administration 268 Byrd-Hagel resolution 268 Canada 189, 195 carbon emissions carbon budget approach 193-7 carbon capture and storage (CCS) 62-7,70,115,264 carbon lock-in framework 101-4 carbon pricing 68-70, 87-93 China's position on 191,201-2,234, 240-46,250-51 India's position on 191, 202 United States' position on 191,201, 242,250-51 see also greenhouse gases (GHG) Carraro, Carlo 6,153,162-3,166, 181 catastrophic events 19-20,32 cement production 111-12, 126 Cerda, Emilio 1, 179 Certified Emissions Reduction units (CERs) 108-9,209,211-23 sectoral mechanisms and discounting 223-30 see also mitigation China Chinese Academy of Social Sciences (CASS) 194,195-7 emissions 191,201-2,250-51 governmental decentralization and 244-6 stance on unilateral cuts 234, 240-43 windpower 115 energy requirements assessed 188-92 statistics on energy and GDP 246-50 energy and coal consumption (1990-2008) 247-9 energy intensity, differentials (2006) 249 energy saving targets: (2006
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.698 | 0.426 |
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".