Carbon Sequestration in Agriculture: EU and US Perspectives La séquestration du carbone en agriculture: perspectives pour l'Europe et les Etats Unis CO<sub>2</sub>‐Sequestrierung in der Landwirtschaft: Perspektiven für die EU und die USA
Bibliographic record
Abstract
summary Carbon Sequestration in Agriculture: EU and US Perspectives Both the European Union and the United States are defining the role that agricultural soil sequestration of carbon will play in their overall strategies to reduce greenhouse gas emissions. These decisions have important ramifications, as recent research indicates that soil sequestration of carbon may have the potential to reduce the need for reductions in greenhouse gas emissions. The EU ratified the Kyoto Protocol in 2002, but chose not to use soil sequestration of carbon in its strategy to address climate change, and has excluded it from the EU's new carbon market. The EU's strategy can be explained by uncertainties surrounding soil sequestration and by the importance of its international leadership on climate change. In contrast, the United States has not ratified the KP, but is encouraging the use of soil carbon sequestration on a modest level, through its agricultural policy and research. There is some trading taking place using soil sequestration in the weak US market for carbon, partially due to US freedom from international protocols on this method of greenhouse gas reduction. As both the science and international protocols for carbon sequestration advance, the EU may reconsider the use of agricultural sequestration of carbon as a means for achieving its Kyoto Protocol commitments.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".