Global Atmospheric Composition Observations: The Heart of Vital Climate and Environmental Action
Bibliographic record
Abstract
Abstract Further long-term investments in high-quality, research-driven, fit-for-purpose observations of atmospheric composition are needed globally to meet urgent societal needs related to weather, climate, air quality, and other environmental issues. Challenges include maintaining current observing systems in the face of eroding budgets for long-term monitoring and filling the geographical gaps for key constituents needed for sound services and policies. The observing systems can be bolstered through science-for-services applications, by embracing interoperable observation systems and standardized metadata, and ensuring that the data are findable, accessible, interoperable, and reusable. There is an urgent need to move from opportunities-driven one-component observations to more systematic, planned multifunctional infrastructure, where the observational data flow is coupled with Earth system models to serve both operational and research purposes. This approach fosters a community where user experience feeds back into the research components and where mature research results are translated into operational applications. This will lead to faster exploration and exploitation of atmospheric composition information and more impactful applications for science and society. We discuss here the urgent need to (i) achieve global coverage, (ii) harmonize infrastructure operations, (iii) establish focused policies, and (iv) strengthen coordination of atmospheric composition infrastructure.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".