Estimates of Emissions from Open Biomass Burning in South-Southeast Asia
Bibliographic record
Abstract
In recent years, fires caused by nature or humans have become the focus of public attention, such as bushfires in Australia ( Celermajer et al., 2021 ) and wildfires in Canada ( Metsaranta et al., 2023 ). Increasingly frequent fires directly cause severe air pollution, which has a huge impact on climate change, human health, etc., and has an increasing impact on developing countries, especially those in South and Southeast Asia (SSEA) ( Reddington et al., 2021 ; Singh et al., 2021 ; Irfan, 2024 ). Extensive Open Biomass Burning (OBB) occurs year-round, leading to widespread exposure to trace gases (CO, NO X , NMVOC, SO 2 , and NH 3 ), particulate matter (PM 2.5 ) levels surpassing World Health Organization (WHO) guidelines ( Linh Thao et al., 2022 ). These burning activities release substantial carbon emissions, negatively affecting not only global climate dynamics but also the health of local inhabitants. Forest clearing, accidental fires, firewood burning, agricultural residue burning, peatland burning, and straw burning are among the significant fire types worldwide (Xu et al., 2022). Moreover, particulate matter and organic carbon emitted from biomass burning adversely affect human health ( Yin, 2020 ). It is an urgent mission to construct an OBB inventory to quantify the local biomass burning’s contribution to regional and global carbon emissions, offering a foundation for devising emission reduction policies and strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".