Atmospheric Sciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science
Bibliographic record
Abstract
Abstract This collaborative article discusses the opportunities and challenges of adopting integrated, coordinated, open, and networked (ICON) principles in atmospheric sciences. From the global nature of the atmosphere, there has always been a need for atmospheric science to be an ICON science. With the help of evolving technology, it is possible to go further in implementing and spreading the ICON principles for productive global collaboration. In particular, technology transfer and applications could be approached with reproducibility in mind, and data‐sharing infrastructure could enable easier and better international collaboration. There are, however, various challenges in following the ICON principles in the acquisition, quality control, and maintenance of data, and the publication of results in a systematic way. Moreover, the extent of such issues varies geographically and hence poses different challenges to implementing ICON principles. In this commentary article, we briefly state our perspectives on the state of ICON, challenges we have met, and future opportunities. Furthermore, we describe how atmospheric science researchers have benefited from these collaborative multi‐dimensional approaches that fulfill the core goal of ICON.
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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 | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".