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Record W4362524257 · doi:10.29303/jgn.v5i1.321

Pengembangan Agroindustri Pengolahan Hasil Pertanian Kelompok Wanitatani di Kecamatan Kediri Kabupaten Lombok Barat

2023· article· en· W4362524257 on OpenAlexaff
‪Dwi Praptomo Sudjatmiko, Muhamad Siddik, Anwar Anwar, Anas Zaini, Bambang Dipokusumo

Bibliographic record

VenueJurnal Gema Ngabdi · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsKelowna General Hospital
Fundersnot available
KeywordsOutreachAgricultural scienceAgricultureBusinessService (business)Production (economics)Product (mathematics)Quality (philosophy)BiotechnologyMarketingGeographyEconomic growthMathematicsBiologyEconomics

Abstract

fetched live from OpenAlex

The income of farm households that rely on the cultivation subsystem has so far been relatively low, so it is necessary to look for additional income such as processing agricultural products such as chilies and tomatoes which are easily damaged. Montong Are Village, Kediri District, has the potential to produce chilies and tomatoes, which when the main harvest prices fall, so it requires yield processing technology that can be obtained from training activities which are part of community service activities. The objectives of this activity are to: (1) increase the knowledge, skills and attitudes of women farming in agricultural product processing activities through training, (2) increase the quantity and quality of processed agricultural products, and (3) produce scientific publications. The target group is a member of a women's group of 30 people from 3 groups in Montong Are Village, Kediri District. The method used in community service activities is participatory based training where women farmers will be actively involved in every activity. Implementation of activities includes: outreach, Focus Group Discussion (FGD), training, and practice of processing agricultural products. The results of the training showed that there was an increase in knowledge for training with chili processing material by 50.45% and for training with tomato processing material there was an increase of 52.25%. In general, there was an increase in the volume of processed chili (sambal clove) and tomato sauce production, although it was not significant because at the time of activity, the production of chili and tomatoes was not much and the price was still high enough that it was not urgent to process them into processed chili/sambal and tomato sauce products. Later, if production is abundant and prices fall, processing of agricultural products, especially chilies and tomatoes, can be carried out.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.004

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 head0.030
GPT teacher head0.215
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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