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Record W4405692349 · doi:10.47006/ijierm.v6i1.338

THE ROLE OF FISHERY EXTENSIONS IN IMPROVING QUALITY CATFISH FARMERS IN BINJAI CITY NORTH SUMATRA PROVINCE

2024· article· en· W4405692349 on OpenAlexaff
M. Nur Nasution

Bibliographic record

VenueInternational Journal of Islamic Education Research and Multiculturalism (IJIERM) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCatfishFisheryQuality (philosophy)GeographyFish <Actinopterygii>BiologyPhysics

Abstract

fetched live from OpenAlex

Abstract: This research aims to investigate and analyze the role of fisheries extension officers in improving the quality of catfish farmers in Binjai City, North Sumatra Province. The quality of catfish farmers is understood as a combination of technical knowledge, practical skills, business management, and the level of innovation acceptance that affects the productivity and sustainability of catfish farming. The research methodology involves field surveys, interviews, and secondary data analysis to understand the conditions of catfish farmers and the effectiveness of the role of fisheries extension officers. The data will be analyzed qualitatively and quantitatively to obtain a holistic picture of the contribution of fisheries extension officers to the improvement of catfish farmer quality. The results of the research are expected to provide in-depth insights into specific aspects that need improvement by fisheries extension officers, such as increasing technical knowledge, implementing more efficient aquaculture practices, and better business management. The implications of this research can be used as a basis for improving fisheries extension programs and sustainable development strategies for catfish farmers in Binjai City and surrounding areas. This research is expected to contribute to the knowledge literature on the role of fisheries extension officers in the context of fisheries management and the empowerment of fishing community

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.048
GPT teacher head0.359
Teacher spread0.311 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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