Bibliographic record
Abstract
Abstract Climate change is tightening its grip on high mountains. Yet, unlike their island counterparts, the ordeals facing mountain communities are under-studied and under-appreciated. But that's about to change. The World Meteorological Organization (WMO) is looking to enable better understanding of the physical processes in mountainous regions, especially their glaciers and ice fields at high elevations, by bringing together meteorological and research communities around the world. This will help identify the key stressors in the mountain environment and facilitate disaster reduction, as well as support decision making and sustainable development. In a forum chaired by David Grimes, WMO’s President, and Tandong Yao, former Director of the Institute of Tibetan Plateau Research, Chinese Academy of Sciences, and co-chair of the Third Pole Environment, a panel of international scientists with diverse backgrounds discussed which priority areas WMO should focus on, how the organization can improve data sharing, how to address climate risks and water scarcity, and how the work can benefit the societal needs of mountain communities. Joan Cuxart Researcher and lecturer on meteorology at the University of the Balearic Islands, Spain Michael Ek Meteorologist at the National Center for Atmospheric Research in Boulder, USA Suhaib Bin Farhan Climate scientist at the Pakistan Space and Upper Atmosphere Research Commission, Pakistan Anil Kulkarni Glaciologist at the Indian Institute of Science, India Soroosh Sorooshian Hydrologist at the University of California Irvine, USA Wenjian Zhang Assistant Secretary-General of the World Meteorological Organization in Geneva, Switzerland; former Deputy Administrator of the China Meteorological Administration, China David Grimes (Chair) President of the World Meteorological Organization in Geneva, Switzerland; assistant deputy minister of Environment Canada, Canada Tandong Yao (Chair) Co-chair of the Third Pole Environment; former Director of the Institute of Tibetan Plateau Research, Chinese Academy of Sciences, China
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".