Building ecosystem resilience for climate change adaptation in the Asian highlands
Bibliographic record
Abstract
The Asian Highlands, the vast mountainous area from Pakistan to China including the Hindu‐Kush Himalaya and Tibetan Plateau, have considerable global importance; they are the source of most of the major rivers of Asia, which sustain billions of downstream dwellers, are part of four Global Biodiversity Hotspots, and support rich cultural diversity. However, climate warming in the Himalaya–Tibetan Plateau has been greater than two times the global average, and regional climate appears to be shifting with potential to trigger large‐scale ecosystem regime shifts (‘landscape traps’). A host of other drivers—urbanization/infrastructure development, land‐use/agricultural practices, upstream/downstream water management and ongoing nation‐state security conflicts—interact with climate signals to produce complex changes across ecological and social systems. In response, highlands people are evolving hybrid forms of adaptive capacity where ‘bottom‐up’ behaviors are mixing with ‘top‐down’ state and market policies. To increase ecosystem and livelihood resilience to future change, there is a need to link upstream and downstream conservation action with local climate adaptation. While the key problem is that institutional and government capacity for coordination is low, we present four general strategies to move forward: application of cross‐sector coordinated planning, strategic integration of science‐based conservation with developing local‐level hybrid knowledge, recognition of the critical role of governance in support of change, and increased emphasis on environmental security. We discuss these strategies for each driver of change in the region. WIREs Clim Change 2014, 5:709–718. doi: 10.1002/wcc.302 This article is categorized under: Climate, Ecology, and Conservation > Conservation Strategies Climate and Development > Social Justice and the Politics of Development
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".