Climate change mitigation and agriculture
Bibliographic record
Abstract
This book reviews the state of agricultural climate change mitigation globally, with a focus on identifying the feasibility, opportunities and challenges for achieving mitigation among smallholder farmers. The purpose is ultimately to accelerate efforts towards mitigating land-based climate change. While much attention has been focused on forestry for its reputed cost-effectiveness, the agricultural sector contributes about ten to twelve per cent of emissions and has a large technical and economic potential for reducing greenhouse gases. The book does not dwell on the science of emissions reduction, as this is well covered elsewhere; rather, it focuses on the design and practical implementation of mitigation activities through changing farming systems. \n \nClimate Change Mitigation and Agriculture includes chapters about experiences in developed countries, such as Canada and Australia, where these efforts also have lessons for mitigation options for smallholders in poorer nations, as well as industrialising countries such as Brazil and China. A wide range of agroecological zones and of aspects or types of farming, including livestock, crops, fish farming, fertilizer use and agroforestry, as well as economics and finance, is included. The volume presents a synthesis of current knowledge and research activities on this emerging subject. Together the chapters capture an exciting period in the development of land-based climate change mitigation as attention is increasingly focused on agriculture's role in contributing to climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".