DINAMIKA PERKEMBANGAN HARGA KOMODITAS CABAI MERAH (Capsicum annuum L) DI KABUPATEN JEMBER
Bibliographic record
Abstract
Red chili prices in the market tend fluctuate over time. Development of red chilli in centers production included at Jember Regency continue to be made, but production still fluctuate and have not been able to meet market demand. Condition fluctuation production with uncertainty price needs to corrected immediately. Information of price and production need as solution to cope with fluctuation of red chili price. The research method used analytical descriptive method. Location chosen by purposive method at Jember Regency. The research used secondary time series data 2012 until 2016. Analytical tools used probability analysis and plot, trend analysis, and multiple linear regression analysis with double log model. The results showed that (1) production and prices red chili fluctuate every quarterly. The highest red chili price at farmers and consumers level occurred in the fourth quarter and the lowest in the second quarter. Red chili highest production in fourth quarter and lowest in first quarter. (2) Trend production and prices of red chili on 2017 until 2018 fluctuated and increased. (3) Factors that significantly affect of red chili supply are red chili production in previous month and harvested area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".