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Record W4401831723 · doi:10.31957/acr.v6i2.3113

Rantai Pasar Ikan Cakalang Asap Pada Masa Pandemi Covid-19 Dan New Normal Di Kota Jayapura

2023· article· en· W4401831723 on OpenAlexaff
Lolita Tuhumena, Basa T. Rumahorbo, Grisella M. S. P. Rewang, Pirhel Pirhel, Vera K. Mandey

Bibliographic record

VenueACROPORA Jurnal Ilmu Kelautan dan Perikanan Papua · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)ChemistryMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The conditions of the Covid-19 pandemic that occurred in 2019-2021 affected the marketing system for Smoked Skipjack. Therefore, the research will be conducted to find out the marketing channels and marketing margins of Smoked Skipjack during the Covid-19 pandemic and the new normal. The research lasted for 6 months at the Mama-mama Papua Market, Hamadi Market, and Cikombong Market. This research method is purposive sampling and be analyzed using descriptive qualitative and quantitative. Based on the research results, it was found that the marketing channel started with fishermen in Dock 9, Jayapura City, who sold their catch to collectors, then collectors sold it to traders who processed fish into smoked fish and smoked fish traders sold it directly to consumers. During the Covid-19 pandemic, the market that had the lowest marketing margin was the Cikombong Market and the highest margin was the Hamadi Market. During the new normal, the highest marketing margin occurred at the Hamadi Market and the lowest was the Cigombong Market, followed by the Mama-Mama Papua Market. During the Covid-19 pandemic and the new normal, efficient markets were the Papua Mama-mama Market, Hamadi Market and Cigombong market, because had <5% efficiency.Then during the new normal, marketing efficiency at Hamadi Market, Papua Mama-mama Market and Cigombong Market were efficient because it had <5% efficiency.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.034
GPT teacher head0.259
Teacher spread0.225 · 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 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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