Wetlands in the Himalaya: Securing Services for Livelihoods - Special Publication
Bibliographic record
Abstract
Wetlands cover 5–10% of the earth’s terrestrial surface. They are important ecosystems that supply goods and services for human wellbeing. Despite their rich biodiversity, social and economic values, wetlands are in immense pressure, and are undergoing constant degradation due to several anthropogenic forces, such as urban development, expansion of agricultural land and industrial pollution. The global extent of wetlands in the 20th century is estimated to have declined by 64–71%, and losses and degradation of wetlands continue worldwide, which will eventually have significant impacts on the supply of ecosystem services and affect the livelihoods of people [Ramsar Secretariat 2015: State of the World’s Wetlands and their Services (Task No. 18)]. In Asia alone, about 5,000 km2 of wetlands vanish each year, with substantial impacts on ecosystem services, biodiversity and the livelihoods of people. In the Hindu Kush Himalaya (HKH) region, there is only limited information available on the overall status of wetlands and resource exploitation because of the difficult geographic terrain and harsh climatic conditions. Thus, to generate a better understanding of wetlands in the region, a common platform was sought to exchange information, learnings and research findings. ICIMOD, in collaboration with the Kunming Institute of Botany (KIB) and the Chengdu Institute of Biology (CIB) under Chinese Academy of Sciences (CAS), and the Yunnan Institute of Environmental Science (YIES), organized a Regional Expert Consultative Symposium on ‘Managing Wetland Ecosystem in the Hindu Kush Himalaya: Securing Services for Livelihoods’ in Dali, Yunnan Province of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".