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PEMANFAATAN PERALATAN PENGOLAHAN IKAN BERBENTUK MESIN UNTUK PENINGKATAN KAPASITAS PRODUKSI PADA KELOMPOK PENGOLAH DAN PEMASAR IKAN DI KAWASAN KONSERVASI PERAIRAN TWP GILI MATRA

2024· article· en· W4391847472 on OpenAlexaff
Soraya Gigentika, Sitti Hilyana, Saptono Waspodo, Paryono Paryono, Muhammad Sumsanto, Martanina Martanina, Rowi Ashari, Lalu Ferdi Alfarisi Murdin, Sadikin Amir, Nurliah Nurliah, Ayu Adhita Damayanti, Ibadur Rahman, Chandrika Eka Larasati, Mahardika Rizqi Himawan, Edwin Jefri, Wiwid Andriyani Lestariningsih, Rhojim Wahyudi, Sholihati Lathifa Sakina

Bibliographic record

VenueJurnal Abdi Insani · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood and Agricultural Sciences
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsFish <Actinopterygii>Environmental scienceAgricultural scienceOperations managementEngineeringFisheryBiology

Abstract

fetched live from OpenAlex

The fish processing and marketing group (poklahsar) in the Gili Matra Marine Protected Area has received intervention from the central government through Bappenas in the COREMAP-CTI III program. This intervention involves improving human resource capacity and providing grants for fish processing equipment. However, the limited duration of the program has resulted in suboptimal assistance in utilizing the donated fish processing equipment. Therefore, the involvement of universities in assisting the community is deemed necessary. This assistance activity aims to inform and educate the poklahsar about the use of fish processing equipment, particularly machines, to enhance production capacity in the Gili Matra Marine Protected Area. The assistance activities were carried out in July 2023 at the respective production locations of each poklahsar. The educational method for adults in this assistance activity involves lectures and hands-on practical training. The outcome of this assistance activity is an increased understanding and knowledge among poklahsar in the Gili Matra Marine Protected Area regarding the functions and uses of various machine-based fish processing equipment. Additionally, it was identified that some obstacles hinder optimizing machine-based equipment utilization, including a lack of understanding in using such equipment and limited market demand for processed fish products. In conclusion, the poklahsar have diverse views on using machine-based equipment, enabling them to optimize the use of such equipment despite the limited demand for processed fish products.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.219
Teacher spread0.203 · 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.

Study designBench or experimental
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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