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Record W4412063659 · doi:10.25077/joseta.v6i2.500

IMPROVING THE DEVELOPMENT OF SMALLHOLDER COCOA PLANTATIONS IN PADANG PARIAMAN DISTRICT

2024· article· en· W4412063659 on OpenAlexaff
Chandra Refolta

Bibliographic record

VenueJOSETA Journal of Socio-economics on Tropical Agriculture · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsAgroforestryBusinessAgricultural economicsGeographyAgricultural scienceEconomicsEnvironmental science

Abstract

fetched live from OpenAlex

Padang Pariaman is one of three regions in West Sumatra designated as a Cocoa Commodity Agricultural Development Area, along with Pasaman Regency and West Pasaman Regency (Kementan RI, 2018). As stated in the Strategic Plan of the Padang Pariaman Regency Agriculture and Food Security Service for 2021-2026, cocoa is one of the leading commodities in the plantation subsector which is a mainstay for Padang Pariaman Regency as a driver of the community's economy. Data from the last few years shows that the area of smallholder cocoa plantations in Padang Pariaman Regency continues to decline (BPS Padang Pariaman, 2024). The decline in cocoa area and production in Padang Pariaman Regency was caused by the lack of management of cocoa plantations by farmers, resulting in attacks by Plant Pest Organisms (OPT) on cocoa plants, one of which was the Phytophthora palmivora fungus (Nasir, 2016). Policy recommendations that can be implemented by the Department of Agriculture and Food Security of Padang Pariaman Regency in an effort to increase the development of smallholder cocoa plantations include; 1). Determine a direction map for increasing the development of smallholder cocoa plantations in Padang Pariaman Regency based on land suitability classes for cocoa plants, and 2). Establish policy guidelines for the use of certified superior seeds for the rehabilitation and rejuvenation of people's cocoa plantations in Padang Pariaman Regency.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.259

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.213
Teacher spread0.192 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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