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Record W1856635177 · doi:10.17528/cifor/005749

Reducing green house gas emissions from oil palm in Indonesia: Lessons from East Kalimantan

2015· report· en· W1856635177 on OpenAlexafffund
Anderson Z.R., Koen Kusters, K. Obidzinski

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsUniversity of Toronto
FundersDirektoratet for UtviklingssamarbeidUniversity of TorontoAustralian Agency for International DevelopmentEuropean CommissionUnited States Agency for International Development
KeywordsPalm oilGreen houseEnvironmental scienceAgricultural economicsGeographyAgroforestryEconomicsHorticultureBiology

Abstract

fetched live from OpenAlex

<b>Key Messages</b> <ul><li>National and provincial emissions reduction goals and efforts to slow deforestation may come into conflict with provincial and district level economic ambitions based on agricultural development.</li><li>Around half of existing oil palm concessions in East Kalimantan are on forested and peatland areas. If developed, these plantations will release ~206 MtCO2e into the atmosphere.</li><li>The expansion of oil palm plantations on currently allocated concessions will lead to the conversion of forested lands and swamp areas, including peatland, and represents a critical source of carbon emissions.</li><li>To ensure the sustainability of plantation expansion the government needs to undertake a review of all existing plantation permits to ensure that they align with existing sustainability criteria.</li><li>Green Growth does not present a win-win strategy and therefore requires strong political commitment, and awareness of social and environmental tradeoffs.</li></ul>

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.050
GPT teacher head0.302
Teacher spread0.252 · 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.

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

Citations0
Published2015
Admission routes2
Has abstractyes

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