El Nino, The 2006 Indonesian Peat Fires, And The Distribution Of Atmospheric Methane
Bibliographic record
Abstract
[1] Dry conditions from a moderate El Nino during the fall of 2006 resulted in enhanced burning in Indonesia with fire emissions of CO approximately 4–6 times larger than the prior year. Here we use new tropospheric methane and CO data from the Aura Tropospheric Emission Spectrometer and new CO profile measurements from the Terra Measurements of Pollution in the Troposphere (MOPITT) satellite instruments with the Goddard Earth Observing System (GEOS)-Chem model to estimate methane emissions of 4.25 ± 0.75 Tg for October–November 2006 from these fires. Errors in convective parameterization in GEOS-Chem, evaluated by comparing MOPITT and GEOS-Chem CO profiles, are the primary uncertainty of the emissions estimate. The El Nino related Indonesian fires increased the tropical distribution of atmospheric methane relative to 2005, indicating that tropical biomass burning can compensate for expected decreases in tropical wetland methane emissions from reduced rainfall during El Nino as found in previous studies.
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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 | 0.000 | 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".