ANALISIS VOLATILITAS HARGA DAGING SAPI MURNI DI PROVINSI JAWA TENGAH DENGAN PENDEKATAN ARCH GARCH
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".