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Record W3194460482 · doi:10.36764/ja.v4i1.306

ANALISIS HUBUNGAN BIAYA PRODUKSI KELAPA SAWIT TERHADAP PENDAPATAN PETANI DI DESA PULO BAYU KECAMATAN HUTABAYU RAJA, KABUPATEN SIMALUNGUN ORGANIK

2020· article· en· W3194460482 on OpenAlexaff
Chris Michael, Posman Hp Marpaung, Fandri Siburian

Bibliographic record

VenueJURNAL AGROTEKNOSAINS · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPalm oilProductivityAgricultural scienceAgriculturePalmPopulationToxicologyGeographyBiologyMedicineEconomics

Abstract

fetched live from OpenAlex

The aim of study is to 1) determine the productivity of oil palm in the study area, 2) determine the income of oil palm farming in the study area and 3) determine the relationship of the cost of oil palm production with the income of oil palm farmers in the study area. This research was conducted in Pulo Bayu village, Hutabayu Raja district, Simalungun regency. The population in this study were farmers who worked on oil palm plants in Pulo Bayu village with a field area ranging from 0.5-10 Ha and the age of the plants between 3.5-18 years. The number of samples in this study were 30 oil palm farmers. Data analysis was performed descriptively and using simple linear regression analysis. The result showed that the productivity of oil palm plants in the study area was classified as low. The production of oil palm farming in the study area is 82,342.80 kg/year or 1,744.76 kg/ ha (1.75 tons/ha/month). This is lower than the average CPO productivity of smallholder estates 2.5 tons/ ha/month. Net income of oil palm farmer in the study area is Rp 1,308,973.06/ha/month is classified as low because it is lower than the UMR of Simalungun regency (Rp 2,224,036.00/ month). Farming production costs significantly have a positive linear effect on the income of oil palm farmers in the study area.

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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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