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Record W4401300782 · doi:10.61194/ijss.v5i3.1253

Cultivating Climate Solutions: Agroforestry’s Potentials and Roles in North Kalimantan’s REDD+ Program

2024· article· en· W4401300782 on OpenAlex
Adi Sutrisno, Wahyu Agang, Tjahjo Tri Hartono, Mas Davino Sayaza

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIlomata International Journal of Social Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAgroforestryClimate changeClimate change mitigationGeographyEnvironmental scienceForestryEcologyBiology

Abstract

fetched live from OpenAlex

Agroforestry in North Kalimantan offers a promising avenue for balancing community livelihoods with carbon sequestration, crucial for the REDD+ initiatives. This paper examines the potential of agroforestry in North Kalimantan to support the REDD+ program, addressing both environmental sustainability and socio-economic development. Through field observations and interviews across four regencies and one city in North Kalimantan province, various agroforestry practices were identified, including improved fallows, alley cropping, scattered trees on cropland, living fences, and silvofishery. Challenges such as cultivation practices, post-harvest processing, market access, and financing were also explored. Three potential agroforestry models were proposed to enhance carbon capture while promoting local economic resilience. The paper underscores the importance of further research and community involvement to refine and expand these agroforestry approaches, offering hope for both local prosperity and global carbon reduction efforts.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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