Bibliographic record
Abstract
Biomass burning is an important source of aerosol emissions that greatly deteriorates air quality near the source and downwind regions. More importantly, large amounts of greenhouse gases (GHGs) such as carbon dioxide (CO2) and methane (CH4) are emitted from wildfires. A quantitative estimate of emissions from biomass burning is vital to understand the fire impacts on climate, weather, environment and public health because wildfires are projected to increase in frequency, severity, and extent in the warming climate. As one of the major climate drivers but with relatively short lifetime in the atmosphere, CH4 is an attractive mitigation target to restrict the pace of global warming. The estimation of spatially and temporally resolved CH4 emissions from the biomass burning sector provides critical information in developing measurement-informed CH4 inventories and assessing mitigation strategies and policy decision making. Satellite observations of fire radiative power is one pathway to investigate wildfires around the world. The Global Biomass Burning Emissions Product (GBBEPx) algorithm is employed to estimate long-term temporal variation and geographic distribution of CH4 emissions using satellite observations from Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi NPP and NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS). Globally, about 23 teragrams of CH4 are emitted from biomass burning every year, with nearly 49% of it released from Africa alone. We will present inter-annual variability of CH4 emissions and describe the magnitude of emissions from notable big fires such as the 2020 gigafire in California and 2023 Canadian fires, to compare and contrast emissions from biomass burning source sector versus various anthropogenic sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".