Priorities for Information, Research and Conservation of Birds in High Mountains
Bibliographic record
Abstract
High mountains cover an estimated 25% of the global land surface, but harbour almost 50% of terrestrial biodiversity hot-spots and about one-third of terrestrial biodiversity globally. Thus, it is concerning that relatively little research has been conducted on birds in high mountains, especially for tropical mountain birds. We identified 10 major knowledge gaps arising from the reviews in our nine previous chapters, including the urgent need for information on avian diversity and population and community ecology, especially in under-studied mountains of the Global South, avian responses to climate change and other stressors, mountains as refugia from habitat and climate change, and the role of protected areas to function as biodiversity reservoirs. We propose a set of priorities for ecological and conservation research and management that will help to ensure persistence of birds in high mountain ecosystems. Maintaining and restoring mountain biodiversity is important from ecological, evolutionary and cultural points of view. We recommend investing in research to safeguard the critical ecological, social and economic values of mountain systems into the future. Strong support is needed from the scientific community, citizen scientists, policy makers, politicians and local communities to fulfill our priorities for the conservation of mountains and mountain birds.
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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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".