Continuation of the MOPITT record of global carbon monoxide
Bibliographic record
Abstract
Measurements Of Pollution In The Troposphere (MOPITT) on the NASA Terra spacecraft has been measuring the global atmospheric abundance of carbon monoxide (CO) since March 2000. Direct emissions of CO are mainly produced by incomplete combustion from both natural fires and anthropogenic activities, and CO is also produced chemically from methane and volatile organic carbon (VOC) species. Although CO has a negligible contribution to greenhouse gas radiative absorption, it does play an important role in atmospheric chemistry and climate because it is a dominant sink for the hydroxyl radical (OH) and thus affects the lifetime of methane (CH4) and production of tropospheric ozone (O3). Because of these interactions, the IPCC AR6 estimated that anthropogenic emissions of CO have a significant indirect radiative forcing of 0.23 W/m2. The MOPITT record is long enough to detect significant trends in atmospheric pollution and assess changes in emissions due to regulations, agricultural burning, technology improvements to combustion efficiency and increasing wildfires due to a warming climate. We will present an overview of the MOPITT data record, the unique combination of thermal and shortwave infrared radiances and discuss the continuation of the MOPITT record with IASI on MetOP, CrIS on SNPP and JPSS satellites and TROPOMI on Sentinel 5P.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".