Are the COP26 Climate Change Negotiations Ready to Embrace Agriculture?
Bibliographic record
Abstract
Summary Even though agricultural and land sector emissions contribute almost one‐quarter of total anthropogenic greenhouse gas emissions to the atmosphere, it has been a long struggle to properly recognise and discuss within the UNFCCC framework the contribution that these sectors can make to the global mitigation effort. This is despite the fact that many countries’ Nationally Determined Contributions highlight the potential for abatement in these sectors, though commitments are often made contingent on receipt of external finance. A major breakthrough occurred with the adoption of the Koronivia Joint Work Programme on Agriculture at COP23 in 2017. The two UNFCCC subsidiary bodies charged with implementing this work programme will report back on its outcomes at the COP26 in Glasgow in November. The UK government’s Campaign for Nature, under its COP Presidency to highlight the importance of nature‐based solutions, can also help to strengthen the focus on the importance of these sectors. This article describes the background to these discussions and discusses possible outcomes at COP26 of relevance to the agriculture and land sectors.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".