EVERGREEN - Global Satellite Observations of Greenhouse Gas Emissions
Bibliographic record
Abstract
The EVERGREEN project, funded by the European Commission 5th Framework Environmental Programme for better exploitation of Earth Observation data, has demonstrated the benefits of new methods for the exploitation of satellite data in global climate and air pollution research and application. In particular, SCIAMACHY on board the European Earth Observation satellite ENVISAT has derived the first greenhouse gas emissions from space, generated ozone measurements to improve the weather forecast and delivered an operational service for air pollution monitoring and predictions.\n\nIn this paper the results of the EVERGREEN project are summarised concentrating on the SCIAMACHY measurements of methane (CH4), carbon dioxide (CO2) and carbon monoxide (CO) in the troposphere. But also the measurements by MIPAS of the vertical distribution of these gases in the upper troposphere and lower stratosphere are analysed. Both SCIAMACHY and MIPAS are spectrometers on board the ESA environmental satellite ENVISAT, which was launched March 1st, 2002 with a scheduled operational life time of 5 years. The measurements by MOPITT, a Canadian instrument on the NASA EOS Terra satellite launched in December 1999, provide additional information on tropospheric carbon monoxide. Global, regional, yearly and seasonal variations of CH4, CO2 and CO over the years 2003-2005 are analysed and compared with atmospheric models and ground based measurements. First inverse modelling studies using the new satellite datasets suggest some significant discrepancies of CH4 and CO emission compared to bottom-up inventories.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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; both teacher heads agree on what is shown here.
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".