The air-sea interface in a changing climate: Research advances and future directions
Bibliographic record
Abstract
At the end of its second decade, the Surface Ocean-Lower Atmosphere Study (SOLAS) continues to expand critical collaborations in Earth system research, opening new gateways between the disciplines of oceanic and atmospheric science. The collection of papers in this Special Feature highlights important recent advances in air-sea interaction science, emphasizing emerging priorities and critical challenges. Since the last SOLAS synthesis in 2014, the community has gained a more nuanced understanding of the variety of marine sources of atmospheric aerosols; the influence of chemical speciation on atmospheric deposition and resulting biogeochemical impacts in the ocean; the mechanistic microscale controls of aerosol production and gas exchange at the sea surface; and also how air-sea exchange processes are influencing and responding to climate change, among numerous other advances. At the same time, SOLAS scientists have engaged more directly with socio-economic networks and in the development and evaluation of environmental and policy decisions. In addition to substantial contributions to improved understanding of the global cycling of greenhouse gases, SOLAS scientists are examining the impacts of new shipping regulations and contributing to development of frameworks for climate intervention research and governance. However, challenges remain, including characterizing the variability in air-sea gas exchange, particularly in coastal regions, and identifying mechanisms by which marine emissions influence cloud dynamics and thereby coupled marine and atmospheric feedbacks to climate change. Addressing these and other challenges requires development of innovative scientific tools (e.g., chemical sensors, expanded and integrated observational networks, machine learning algorithms), and also new inter- and trans-disciplinary collaborations, to ensure that air-sea exchange research continues to transcend boundaries in tackling current and emerging global challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".