Bioenergy for reducing greenhouse-gas emissions
Bibliographic record
Abstract
Bioenergy, based on renewable biomass, can play a significant role in mitigation of greenhouse gas (GHG) emissions. The International Energy Agency Bioenergy Task 38 'Greenhouse Gas Balances of Biomass and Bioenergy Systems' is an international collaborative network that develops methodologies for assessing GHG balances of bioenergy systems. Countries participating in Task 38 are: Austria, Australia, Canada, Denmark, Finland, Ireland, The Netherlands, New Zealand, Norway, Sweden and the USA. The Task has developed computer models and case studies for the assessment of GHG balances of bioenergy systems compared with fossil energy systems. A major objective is to aid decision-makers in selecting the most effective options to limit emissions or enhance removals of greenhouse gases. Exchange of models, ideas and experience is facilitated through regular workshops. Position papers and joint publications on the role of bioenergy and carbon sinks in greenhouse gas mitigation have been prepared. For example, Task 38 has produced a 'Frequently Asked Questions' paper on bioenergy, carbon sinks and global climate change. The Task also contributes to the work of the Intergovernmental Panel on Climate Change, to clarify the biomass (sinks) provisions of the Kyoto Protocol.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.021 |
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".