Bibliographic record
Abstract
Mountains cover 24% of the Earths land surface, are home to 12% of the global population, and include 28% of the worlds forests. Mountain forests provide a wide range of benefits to both mountain and downstream populations, notably the protection of watersheds and of transport infrastructure. They are also important as centres of biodiversity; important sources of timber, fuelwood and non-wood products; places for tourism and recreation; and sacred places. Many are also being considered as possible carbon sinks to mitigate climate change. Mountain forests are subject to many forces of change, interacting in complex ways. The frequency of natural disturbances is increasingly influenced by human activities at local, regional, and global scales. Air pollution has influenced many forests downwind of industrial areas, but climate change represents a greater and highly unpredictable force for change. It will require new types of decisions by all stakeholders, and new forest management approaches and policies. The International Year of Mountains, 2002, presents a unique opportunity to foster greater co-operation to ensure that mountain forests continue to provide benefits to a significant proportion of the worlds population well into the 21st century and beyond. Key words: forests, sustainable development, mountains, climate change, co-operation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".