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Record W2619732400 · doi:10.3390/f8060188

Post-Fire Restoration Plan for Sustainable Forest Management in South Korea

2017· article· en· W2619732400 on OpenAlexaff
Soung‐Ryoul Ryu, Hyung–Tae Choi, Joo-Hoon Lim, Im-Kyun Lee, Young Sang Ahn

Bibliographic record

VenueForests · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Alberta
FundersNational Research Foundation of Korea
KeywordsReforestationForest restorationGeographyRestoration ecologyAfforestationForest managementPrescribed burnAgroforestryFire preventionRecreationVegetation (pathology)Erosion controlDisturbance (geology)Environmental protectionForest ecologyEnvironmental scienceEcosystemForestryErosionEcology

Abstract

fetched live from OpenAlex

This review was to determine a standard post-fire restoration strategy for use in South Korea according to the magnitude of the damage and the condition of the affected site. The government has strongly enforced reforestation in deforested areas as well as fire prevention and suppression since the 1960s. These efforts have successfully recovered dense even-aged forests over the last five decades. However, high fuel loading and the homogeneous structure have made forests vulnerable to large fires. In recent years, large forest fires have occurred in the eastern coastal region of Korea. Forest fires can significantly influence the economic and social activities of the residents of such affected forest regions. Burned areas may require urgent and long-term restoration strategies, depending on the condition of the affected site. Erosion control is the most important component of an urgent restoration and should be completed before a rainy season to prevent secondary damage such as landslides and sediment runoff in burned areas. Long-term restoration is necessary to renew forest functions such as timber production, water conservation, ecosystem conservation, and recreation for residents. Sound restoration for burned areas is critical for restoring healthy ecological functions of forests and providing economic incentives to local residents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.989

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 teacher head, not a consensus.

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

Citations30
Published2017
Admission routes1
Has abstractyes

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