Bibliographic record
Abstract
adaptation concept of 1-2 African, Caribbean and Pacific (ACP) countries 52, 57 exports to Europe 52-3 Argentina 95 CDM abatement potential 97, 99, 103, 105 Australia 38, 41 government of 41 Bangladesh PoAs in 30 Belarus 38 Brazil 5, 27 abatement potential of 77 CER supply potential 29 government of 117 Programa de Incentivo às Fontes Alternativas de Energia Elétrica (PROINFA) 117 Canada 6, 41 carbon capture and storage (CCS) 8, 42, 46, 76, 106, 164 Central Intelligence Agency (CIA) World Factbook 129 certified emission reduction credits (CERs) 7, 12, 22, 30-33, 39, 41, 49-51, 56, 75, 163 buyers of 63 decline in price 6, 113 demand scenarios 39, 41-2, 48-9 EU ban on (2011) 38, 49 forestry 55 generation of 44-6, 93 import of 42 restrictions on 28, 36, 49, 163 supply in LDCs 30-32, 46, 49-51 use in CDM 3-4, 29 China 5, 27, 68, 72-3, 75, 82, 92, 95, 99, 101, 105 abatement cost curve of 78 abatement potential of 77, 79, 97, 101, 103 as major CDM host 66 CER supply potential 29 energy sector of 69 power tariffs in 116 Clean Development Mechanism (CDM) 3-15, 17-39, 42-6, 48-51, 54, 56-7, 59-65, 67-9, 71-2, 74-6, 78-9, 82-3, 86-95, 99, 101, 103, 105-6, 111-15, 120, 123-7, 130-33, 138-41, 144-7, 162-9 Clean Development Mechanism investment 120-21 democratic influence on 122 Clean Development Mechanism project documentation (PDD) 35, 44-5, 67 development of 51, 53, 57 financial information 88 Climate Change Convention 2-3 Ad-Hoc Working Group on Long-term Cooperative Action under the Convention (AWG-LCA) 8 shortcomings of 38 component project activities (CPAs) 30, 33 examples of 51 Conference of the Parties/Meeting of the Parties (COP/MOP) 7, 55 Bali Action Plan (2007) 2, 8 COP 15 (Copenhagen) 35-6 COP/MOP 2 (Nairobi) 32 Nairobi Framework (2006) 32 revisions to CDM 7-9, 27, 39 Croatia 38 Denmark 118 Copenhagen 2, 6, 35 designated national authority (DNA) 130, 141, 144 establishment of 34 project approval process 5, 123 projects rejected by 5 use in CDM mechanism 3, 5, 33 designated operational entity (DOE) 3-4 rejection of projects by 45 discounting emission credits 64, 66, 81-3, 93 demand-side 63 discussions regarding 61-2 supply-side 63
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.019 |
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; both teacher heads 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".