Making best use of atmosphere- and inventory-based approaches for quantifying and understanding emissions of greenhouse gases and ozone-depleting substances on a range of spatial scales.
Bibliographic record
Abstract
The likelihood for successful emission control and mitigation efforts of trace gases having adverse environmental effects can be enhanced by using a multi-faceted framework for quantifying and understanding emissions. While bottom-up activity-based inventories provide a quantification of various source sectors, appropriately designed atmosphere-based (top-down) approaches are able to independently evaluate the inventory and further refine temporal changes and spatial distributions. Differences between bottom-up and top-down estimates are oftentimes observed and represent prime opportunities for increasing understanding and refining estimates of emissions. Here we will present results derived from atmospheric observations made in the remote global atmosphere as well as from our North American measurement network. The remote global observations enabled the identification of an apparent violation of the Montreal Protocol. After our atmospheric measurements identified this unexpected issue, fairly quick resolution appears to have been achieved, in part due to the additional understanding of likely underlying causes provided by industry experts. Our North American measurement network also allows for trace-gas emission estimates on national and state scales. Results from these efforts will be discussed, with an emphasis on describing how the interaction between inventory-derived and atmosphere-based information has led to an improved understanding of emission magnitudes along with identifying areas needing additional study.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".