Quantifying CO emissions from the 2004 Alaskan wildfires using MOPITT CO data
Bibliographic record
Abstract
We present an inverse model analysis to quantify the emissions of wildfires in Alaska and Canada in the summer of 2004 using carbon monoxide (CO) data from the Measurements of Pollution in the Troposphere (MOPITT) remote sensing instrument together with the chemistry transport model MOZART (Model for Ozone and Related Chemical Tracers). We use data assimilation outside the region of the fires to optimally constrain the CO background level and the transport into that region. Inverse modeling is applied locally to quantify the fire emissions. Our a posteriori estimate of the wildfire emissions gives a total of 30 ± 5 Tg CO emitted during June–August 2004 which is of comparable order to the anthropogenic emissions for the continental US. The simulated CO fields have been evaluated by comparison with MOPITT and independent aircraft data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".