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Record W2086655015 · doi:10.5558/tfc79219-2

Why mountain forests are important

2003· article· en· W2086655015 on OpenAlexvenueno aff
Martin F. Price

Bibliographic record

VenueThe Forestry Chronicle · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeRecreationGeographyBiodiversityPopulationTourismMountain range (options)Environmental resource managementGlobal warmingEnvironmental protectionEnvironmental scienceBusinessEcology

Abstract

fetched live from OpenAlex

Mountains cover 24% of the Earth’s land surface, are home to 12% of the global population, and include 28% of the world’s 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 world’s population well into the 21 st 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.010
GPT teacher head0.234
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2003
Admission routes1
Has abstractyes

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