Bibliographic record
Abstract
Abstract The El Niño‐driven fire season in Indonesia in September–October 2015 produced the largest fire emissions on record since NASA's EOS satellites started making observations of tropospheric pollutants from space. In this study, measurements of carbon monoxide (CO) from the Measurement of Pollution in the Troposphere (MOPITT) on Terra and the Microwave Limb Sounder are used to characterize the anomalously high CO emitted during the 2015 Indonesian fire season transported into the tropical upper troposphere and stratosphere. The satellite measurements indicate that CO emitted from wildfires was transported into the upper troposphere with time lags up to ∼2 months and continued to be transported into the stratosphere, which resulted in higher concentrations of CO extending up to ∼20 hPa by the end of 2016. Hydrogen cyanide (HCN) measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE‐FTS) confirms that anomalously high HCN emitted from the same wildfires was also transported into the tropical stratosphere and persisted throughout 2017. Simulations of CO from the Community Atmosphere Model with Chemistry (CAM‐chem) show a significant increase in CO concentrations in the troposphere in October 2015. However, comparisons between CAM‐chem and MOPITT CO suggest that the model underestimates the amount of CO even with doubled emissions of CO in October 2015. Both the satellite measurements and the model simulations show that the pollution emitted from the wildfires over Indonesia was transported to and persisted in the tropical stratosphere much longer than the previous El‐Niño driven fire events due to unprecedented amount of the fire emissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".