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ANALISIS VOLATILITAS HARGA DAGING SAPI MURNI DI PROVINSI JAWA TENGAH DENGAN PENDEKATAN ARCH GARCH

2022· article· en· W4293213436 on OpenAlex
Anita Sandiarti, Yustirania Septiani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Jendela Inovasi Daerah · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock Farming and Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsVolatility (finance)Autoregressive conditional heteroskedasticityCommodityArchEconomicsJavaEconometricsGeographyComputer scienceFinance

Abstract

fetched live from OpenAlex

Indonesia has an important commodity for the community, namely beef including in Central Java Province. One of the foodstuffs that produce protein is beef where its usefulness is important to meet human nutritional needs. Besides being important for consumption needs, this commodity also contributes in economic terms because beef is produced by the community ranging from small to large scale. This research further leads to reviewing the volatility of beef prices in Central Java Province through the ARCH GARCH method and daily data (time series) of beef on January 1, 2020 to December 31, 2020. The results of the study showed the most appropriate model for calculating the volatility of beef prices is the model (1,2). The results of the model predictions show that the movement of beef price volatility tends to be stable when after eid al-Fitr, and it is expected that changes or spikes in beef prices in the future will be less minimal.

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.

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 categoriesScience 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 score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.218
Teacher spread0.195 · 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