Developing National Baseline GHG Emissions and Analyzing Mitigation Potentials for Agriculture and Forestry Using an Advanced National GHG Inventory Software System
Bibliographic record
Abstract
National greenhouse gas (GHG) inventories are compiled by governments for all sectors, including agriculture and forestry, and provide historical emissions data for assessing Nationally Appropriate Mitigation Actions (NAMAs). National inventory guidelines have been developed by the Intergovernmental Panel on Climate Change (IPCC), and the Agriculture and Land Use National Greenhouse Gas Inventory (ALU) software is an advanced system applying the IPCC inventory methods, along with functionality to assess GHG mitigation potentials. Two GHG mitigation case studies are provided for rice (Oryza sativa L.) management in the Philippines and livestock and manure management in South Africa. The Philippines example demonstrates that managing rice straw by burning the residues will decrease GHG emissions, relative to the current practice of leaving some rice straw on the field. Rice straw is a substrate that enhances methane emissions when the soil is flooded, and it is a source of nitrogen leading to soil nitrous oxide emissions. However, this option may not be a NAMA because retaining some residue in the field is important for longer-term sustainability of the soil for crop production, as well as the negative effects of burning residues on air quality and human health. For South Africa, enteric methane emissions can be reduced with feed additives in ruminant diets that are managed in confined operations. In addition, GHG emissions from manure management can be reduced with the more widespread use of digesters. The ALU software provides a framework to quantitatively assess mitigation options in a country and inform policy decisions about NAMAs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".