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Record W2901822226 · doi:10.5539/mas.v12n12p41

Farmers’ Income and Land Cover Change at Lore Lindu National Park in Indonesia

2018· article· en· W2901822226 on OpenAlexvenueno aff
Rustam Abd. Rauf, Adam Malík, Isrun, Golar Golar, Alimudin Laapo, Marzuki Marzuki, Sri Ningsih, Arung Gihna Mayapada, Effendy

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureNational parkGeographyLand coverForest coverAgroforestrySecondary forestLand useForestryShifting cultivationEcologyEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Lore Lindu National Park in Central Sulawesi is one of the protected areas. Although it is protected, many of its areas are experiencing pressure and disruption by human activities. The purpose of this research was to know the relationship between farmers’ income and land cover change at Lore Lindu National Park. The research method was a participatory survey. Variables of incomes were sourced from farming and outside farming (forests). Data of land cover change were obtained using imagery in 2012, 2014 and 2016. The result of research is the average of farming income of IDR 1,387,077 (cocoa farming) and non-farm income of IDR 854,819 (forest honey, resin). In the last five years (2012-2016), the primary forest area decreased by 902.37 ha, while the secondary forest has shown an increase of 2,233.61 ha. The trend of land cover change was drastic, with change in the secondary dry land forest area from 6.9 ha (in 2012) to 2,240.5 ha (in 2016). The increased secondary forest area and the secondary forests have been converted into agricultural land and mixed gardens.

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 score0.017
Threshold uncertainty score0.395

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.025
GPT teacher head0.221
Teacher spread0.196 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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