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Record W2042049379 · doi:10.5558/tfc82031-1

Need for sustainability policy – a case study of the Natural Forest Conservation Program (NFCP) in the western region of Tianshan Mountain, China

2006· article· en· W2042049379 on OpenAlexvenueno aff
Qiang Wang, Xiao Ruan, Pan Cun-de, Ningyi Xu, Xia Luo, Min-Ming Huang

Bibliographic record

VenueThe Forestry Chronicle · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSustainabilityLoggingChinaGeographyNatural forestBiodiversityForest managementNatural (archaeology)Sustainable managementSustainable forest managementAgroforestryEnvironmental resource managementEnvironmental protectionForestryEcologyEnvironmental science

Abstract

fetched live from OpenAlex

A case study of the Natural Forest Conservation Program (NFCP) in the western region of Tianshan Mountain was undertaken between 2001 and 2003. Data on the geographic, climatic, ecological, and social economy background of the study area were collected. The impact of the NFCP on the forest, soil, water, and biodiversity in the study area were analyzed and evaluated. The results show that a complete logging ban in the study area cannot replace sustainable forest management; a more flexible policy should be adopted to resolve technical, social and economic problems associated with the complete and suddenly implemented logging ban. In order to strengthen the long-term sustainability of the NFCP, public awareness and funding support China should be increased. Key words: sustainability, Natural Forest Conservation Program, NFCP, forest management, Tianshan Mountain

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.277
Teacher spread0.266 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations10
Published2006
Admission routes1
Has abstractyes

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