Fostering multidisciplinary research on interactions between chemistry, biology, and physics within the coupled cryosphere-atmosphere system
Bibliographic record
Abstract
The cryosphere, which comprises a large portion of Earth’s surface, is rapidly changing as a consequence of global climate change. Ice, snow, and frozen ground in the polar and alpine regions of the planet are known to directly impact atmospheric composition, which for example is observed in the large influence of ice and snow on polar boundary layer chemistry. Atmospheric inputs to the cryosphere, including aerosols, nutrients, and contaminants, are also changing in the anthropocene thus driving cryosphere-atmosphere feedbacks whose understanding is crucial for understanding future climate. Here, we present the Cryosphere and ATmospheric Chemistry initiative (CATCH) which is focused on developing new multidisciplinary research approaches studying interactions of chemistry, biology, and physics within the coupled cryosphere – atmosphere system and their sensitivity to environmental change. We identify four key science areas: (1) micro-scale processes in snow and ice, (2) the coupled cryosphere-atmosphere system, (3) cryospheric change and feedbacks, and (4) improved decisions and stakeholder engagement. To pursue these goals CATCH will foster an international, multidisciplinary research community, shed light on new research needs, support the acquisition of new knowledge, train the next generation of leading scientists, and establish interactions between the science community and society.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".