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Record W4410521789 · doi:10.32528/agribest.v7i2.20970

Marketing And Business Of "Plastic Flowers" In Kurulu District, Jayawijaya Regency

2023· article· en· W4410521789 on OpenAlexaff
Alber Tulak, Anti Uni Mahanani, Susie Suryani

Bibliographic record

VenueJurnal Agribest · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsSt. Peter's Hospital
Fundersnot available
KeywordsBusinessMarketing

Abstract

fetched live from OpenAlex

Jayawijaya Regency is one of the regencies in the province of Papua which has the potential to develop ornamental plants, one of which is "Plastic Flowers". Many local and foreign tourists buy this flower as a souvenir. The aims of this study are: 1) To obtain information and the potential for the development of the "Plastic Flower" agribusiness in Jayawijaya Regency; 2) To find out the income of the "Plastic Flower" farming business in Jayawijaya Regency. The research was conducted in Kurulu District. The time of the research is from June to August 2022. The determination of the research location is purposive. The data collected in this study are in the form of Focus Group Discussion (FGD) and survey methods by conducting interviews, distributing questionnaires, and collecting secondary data (documents) from farmers, business actors, and other stakeholders at the "Bunga Plastik" agribusiness center in Kurulu District. Jayawijaya Regency. Based on the study results, it can be concluded: 1) Information and potential for developing "Plastic Flowers" during the planting season from June to August 2022 is 417 bunches. With a price of Rp. 25,000, revenue of Rp. 10,425,000 is obtained with a production cost of Rp. 3,207,600. Then an income of Rp. 7,217,400 is obtained. "Plastic Flower" has the potential to be developed; 2) R/C ratio analysis which states that the contribution of the total costs incurred to total revenue is 7,91. It means that "Plastic Flower" farming in Kurulu District, Jayawijaya Regency is profitable for farmers.

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)0.003
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 score0.074
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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
Published2023
Admission routes1
Has abstractyes

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